Reviewerly/exHarmony huggingface dataset:
Quantum low-density parity-check codes

This chapter is devoted to quantum low-density parity-check (LDPC) codes, which have many advantages over other classes of quantum codes thanks to the sparseness of their quantum-check matrices. Both semirandom and structured quantum LDPC codes are described. Key advantages of structured quantum LDPC codes over other codes include (i) a regular structure in corresponding parity-check (H-) matrices, leading to low-complexity encoders/decoders, and (ii) sparse H-matrices that require a small number of interactions per qubit to determine error location. The chapter begins by introducing classical LDPC codes, their design, and decoding algorithms. Further, the dual-containing quantum LDPC codes are described. The next section is devoted to entanglement-assisted quantum LDPC codes. Further, the probabilistic sum-product algorithm is described based on a quantum-check matrix instead of a classical parity-check matrix. Note that encoders for dual-containing and entanglement-assisted quantum LDPC codes can be implemented based on standard form or conjugation methods (described in Chapter 8). Since there is no difference in the encoder implementation of quantum LDPC codes compared with other classes of quantum block-codes described in Chapter 8, we omit a discussion on encoder implementation and concentrate instead on design and decoding algorithms for quantum LDPC codes. Quantum spatially coupled LDPC codes are briefly described in the last section.
Basics of quantum information, quantum communication, quantum sensing, and quantum networking

In this chapter, we provide an overview of quantum communications (QuComs), quantum error correction (QEC), quantum networks, and quantum sensing (QuSen) and discuss their relevance. We then discuss the basic principles of QuComs, QEC, QuNets, and QuSen. The last section of the chapter is devoted to the organization of the book, with a detailed description of each chapter.
Algebraic Presentation of Semifree Monads

Monads and their composition via distributive laws have many applications in program semantics and functional programming. For many interesting monads, distributive laws fail to exist, and this has motivated investigations into weaker notions. In this line of research, Petrişan and Sarkis recently introduced a construction called the semifree monad in order to study semialgebras for a monad and weak distributive laws. In this paper, we prove that an algebraic presentation of the semifree monad $$M^{\mathrm {s}}$$ on a monad M can be obtained uniformly from an algebraic presentation of M. This result was conjectured by Petrişan and Sarkis. We also show that semifree monads are ideal monads, that the semifree construction is not a monad transformer, and that the semifree construction is a comonad on the category of monads.
Generating environmental data: Progress and shortcoming

Environmental data is that which is based on the measurement of environmental parameters, and its impacts on the ecosystems. Environmental data is a set of quantitative, qualitative, or geographically referenced facts about the state of the environment and how it is changing. There are progress made that needs more improvement and shortcomings that requires urgent action. This chapter is an overview of the progress and shortcoming made in data curation in environmental research.
Predicate and Relation Liftings for Coalgebras with Side Effects: An Application in Coalgebraic Modal Logic

We study coalgebraic modal logic to characterise behavioural equivalence in the presence of side effects, i.e., when coalgebras live in a (co)Kleisli or an Eilenberg-Moore category. Our aim is to develop a general framework based on indexed categories/fibrations that is common to the aforementioned categories. In particular, we show how the coalgebraic notion of behavioural equivalence arises from a relation lifting (a special kind of indexed morphism) and we give a general recipe to construct such liftings in the above three cases. Lastly, we apply this framework to derive logical characterisations for (weighted) language equivalence and conditional bisimilarity.
Quantum key distribution

This chapter is devoted to the fundamentals of quantum key distribution (QKD). The chapter begins with a description of key differences between conventional cryptography and QKD. In the section on QKD basics, we review various types of QKD. We then describe two fundamental theorems on which QKD relies: the no-cloning theorem and the theorem of the inability to unambiguously distinguish nonorthogonal quantum states. In the section on discrete-variable QKD (DV-QKD) systems, we describe BB84, B92, Ekert (E91), Einstein–Podolsky–Rosen (EPR), and time-phase encoding protocols. In the section on QKD security, the secret-key rate (SKR) is represented as the product of the raw key rate and fractional rate. A generic expression for fractional rate is provided, followed by a description of different eavesdropping strategies, including individual, collective, coherent, and quantum hacking/side-channel attacks. For individual and coherent attacks, corresponding secret fraction expressions are provided. Decoy-state protocols are then described together with the corresponding SKR calculation. Next, key concepts for measurement-device-independent QKD (MDI-QKD) protocols are introduced, including polarization-based and time-phase encoding-based MDI-QKD protocols as well as secrecy fraction calculations. Further, twin-field QKD protocols are described, and their performance is evaluated against decoy-state and MDI-QKD protocols. The focus then moves to information reconciliation and privacy amplification steps. In the section on continuous-variable QKD (CV-QKD) protocols, homodyne and heterodyne detection schemes are described first, followed by a brief description of squeezed state-based protocols. Coherent states are much easier to generate and manipulate, so coherent state-based protocols are described in detail. For lossy transmission channels, corresponding covariance matrices are derived for homodyne and coherent detections schemes, followed by SKR derivation for prepare-and-measure Gaussian modulation-based CV-QKD. Some illustrative SKR results are provided for Gaussian modulation-based CV-QKD schemes.
Quantum stabilizer codes and beyond

This chapter describes a well-studied class of quantum codes, namely stabilizer codes and related quantum codes. The basic concept of stabilizer codes was introduced in Chapter 7. Here stabilizer codes are described more rigorously. After introducing stabilizer codes, their properties and encoded operations are introduced. Further, stabilizer codes are described using finite geometry interpretation. The standard form of stabilizer codes is introduced and used to provide efficient encoder and decoder implementations in a subsequent section. Nonbinary stabilizer codes are then described. Next, subsystem codes are introduced and described, followed by the topological codes. The section after that relates to surface codes. Entanglement-assisted codes are described in the final section.
Corecursion Up-to via Causal Transformations

Up-to techniques are a widely used family of enhancements of corecursion and coinduction. The soundness of these techniques can be shown systematically through the use of distributive laws. In this paper we propose instead to present up-to techniques as causal transformations, which are a certain type of natural transformations over the final sequence of a functor. These generalise the approach to proving soundness via distributive laws, and inherit their good compositionality properties. We show how causal transformations give rise to valid up-to techniques both for corecursive definitions and coinductive proofs.
An Evaluation of Predictor Variables for Photovoltaic Power Forecasting

Accurate forecasts of the electric power generation by solar Photovoltaic (PV) systems are essential to support their vast increasing integration. This study evaluates the interdependence of 14 predictor variables and their importance to machine learning models that forecast the day-ahead PV power production. To this purpose, we use two feature selection models to rank the predictor variables and accordingly, examine the performance change of two forecast models when a growing number of variables is considered. The study is performed using 3 years of data for Utrecht, the Netherlands. The results show the most important variables for PV power forecasting and identifies how many top variables should be considered to achieve an optimal forecast performance accuracy. Additionally, the best forecast model performance is found when only a few predictor variables are considered, including a created variable that estimates the PV power output based on technical system characteristics and physical relations.
Micro–Macro Changepoint Inference for Periodic Data Sequences

Existing changepoint approaches consider changepoints to occur linearly in time; one changepoint happens after another and they are not linked. However, data processes may have regularly occurring changepoints, for example, a yearly increase in sales of ice-cream on the first hot weekend. Using linear changepoint approaches here will miss more global features such as a decrease in sales of ice-cream due to other product availability. Being able to tease these global changepoint features from the more local (periodic) ones is beneficial for inference. We propose a periodic changepoint model to model this behavior using a mixture of a periodic and linear time perspective. Built around a Reversible Jump Markov chain Monte Carlo sampler, the Bayesian framework is used to study the local (periodic) changepoint behavior. To identify the optimal global changepoint positions we integrate the local changepoint model into the pruned exact linear time (PELT) search algorithm. We demonstrate that the method detects both local and global changepoints with high accuracy on simulated and motivating applications that share periodic behavior. Due to the micro–macro nature of the analysis, visualization of the results can be challenging. We additionally provide a unique perspective for changepoint visualizations in these data sequences. Supplementary Materials for this article are available online.
Big Data, Analytics, Transparency and Quality of Experience

We have previously discussed big data analytics as part of smart information systems, but we will revisit them in this chapter in tandem with the idea of transparency and how this affects the overall quality of experience of the users in smart information systems. Previously, we have looked at the data itself and the motivation for the use of synthetic data, whereas now we will approach the area of big data and big data analytics more holistically. In fact, when we say big data, we are referring to extremely large datasets that are quite challenging to work with with traditional database management tools. These have provided new opportunities but also new risks explored in this chapter, along with how this is experienced from the user perspective in terms of trust.
Perception of letter glyph parameters for InfoTypography

The advent of variable font technologies---where typographic parameters such as weight, x-height and slant are easily adjusted across a range---enables encoding ordinal, interval or ratio data into text that is still readable. This is potentially valuable to represent additional information in text labels in visualizations (e.g., font weight can indicate city size in a geographical visualization) or in text itself (e.g., the intended reading speed of a sentence can be encoded with the font width). However, we do not know how different parameters, which are complex variations of shape, are perceived by the human visual system. Without this information it is difficult to select appropriate parameters and mapping functions that maximize perception of differences within the parameter range. We provide an empirical characterization of seven typographical parameters of Latin fonts in terms of absolute perception and just noticeable differences (JNDs) to help visualization designers to choose typographic parameters for visualizations that contain text, as well as support typographers and type designers when selecting which levels of these parameters to implement to achieve differentiability between normal text, emphasized text and different headings.
The Effect of Using Somatic Auditory Visual Intellectual (SAVI) Method on students’ Achievement in Writing Analytical Exposition Text

This study deals with the effect of using Somatic Auditory Visual Intellectual (SAVI)method on students’ achievement in writing analytical exposition text. The objective of thestudy isto find out whether SAVI method significantly affects the students’ achievement in writinganalytical exposition text or not. This study was conducted by using experimental group andcontrol group. The population of the study was the students of grade XI of SMK Taman siswaMedan in the academic year 2015-2016, where there were 2 classes of XI AdministrasiPerkantoran writer taught by applying lecturing method and at XI Administrasi Perkantoran 2taught by applying SAVI method with the sample by purposive sampling. The data of the studywere obtained from the students’ score of writing test. The writer used Pearson Moment Productformula. They were two data used in this study. They were pre-test and post-test. The data wereanalyzed by applying t-test formula. After analyzing the data, the result of the study showed that tobserved (2,81) was higher than t-table (0,05) (t-observed &gt; t-table) at the level of significance of a= 5% and at the degree of freedom (df) = 0,5. it can be concluded that applying SAVI methodsignificantly affects on the students’ achievement in writing analytical exposition text or in theother words the hypothesis is accepted.
The Effectiveness of Dynamic Governance in Peer-to-peer Platforms: An Agent-Based Simulation

With the widespread use of social media, there is a need for content moderation, that is, regulating radical content while ensuring freedom of speech. This study uses agent-based simulation to analyze the fine-tuning of governance in such peer-to-peer platforms. Specifically, we examined the impact of three governance scenarios (open, closed, and dynamic) with varying levels and timing of participation constraints on the outcomes of quantity, quality, and diversity of participants, as well as the performance of platform companies. Comparing those scenarios shows that dynamic governance, which has a better balance of outcomes, results in the highest company performance. This result shows the significance of not only the level of constraints, but also the timing of their modification. This study also points to new research directions, such as diversity within platforms and distributed content moderation.
Classification and Base Rate of Destructive Follower Behavior: A Mixed-Methods Approach

Anecdotes from the daily working lives of leaders often include reports of destructive followers, such as employees intentionally underperforming, blaming others for their own mistakes, or undermining the leader’s authority by constantly doubting the leader’s decisions. However, such real-life experiences of many leaders who are confronted with destructive follower behaviors have not yet been reflected in a rigorous scholarly understanding of the behavioral building blocks of this phenomenon and its prevalence. Accordingly, we aimed to develop a classification of destructive follower behaviors that can guide future research on this multifaceted phenomenon. To achieve this goal, we conducted 40 semi-structured interviews with organizational leaders and utilized topic modeling to map out five categories of destructive follower behaviors (i.e., entitlement, contact seeking/avoiding, effort minimization, emotionally fluctuating communication, and undermining team cohesion). We then conducted a second study with 1,229 leaders to obtain an impression of the base rate of the phenomenon (i.e., how often do these categories of destructive follower behaviors occur?) and exploratively assessed leader-level correlates. We outline how our categorization and the explored correlates can build the foundation for behavioral coding methods and rigorous experiments. We also discuss potential consequences of destructive followership and reflect on role spillover effects.
The Effect of CEO Cognitive Simplicity on Firm-Level Strategic Dynamism

Management scholars have pointed to the cognitive models of the chief executive officer (CEO) as an integral factor for strategic decision-making, generally assuming that with more simple models, strategic change and dynamism are impeded. We question this assumption and posit that it is, in fact, CEO cognitive simplicity that will facilitate strategic change. Building upon a novel linguistic tool based on CEOs' language patterns in the question-and-answer (Q&A) parts of earnings calls, we argue that CEO cognitive simplicity positively affects firm-level strategic dynamism. Moreover, we put forward a contingent view of this relationship and introduce CEO overconfidence and the firm's information-processing load (IPL) as crucial influences. Based on our Sample of Standard & Poor's 1500 (S&P 1500) firms, we find substantial evidence for our theorizing. In our firm-fixed effects regression and two-stage least square fixed-effects instrumental variable regression (2SLS FE), CEO cognitive simplicity positively affects firm-level dynamism. Moreover, overconfidence attenuates the CEO's cognitive simplicity-strategic dynamism relationship, while a decreasing IPL strengthens it. We conclude that, contrary to the assumption of cognitively simple models making organizations more resistant to strategic change (Miller, 1991, 1993), the opposite holds true. This carries substantial implications for management literature.
Information Security Risk Perception and Firm Behaviour

The escalating information security threats and their economic and other impacts have made firms pay careful attention to potential risks they face and the actions they can take to mitigate such risks. In this paper, we explore if and how the information security risk perceptions of firms shape their boundary changing behaviors. We argue that organizations have risk transfer, risk avoidance, risk reduction, risk acceptance options, and combine these options in their attempts to reduce the perceived effects of information security risks. Organizations through risk hedging could transfer some of the information security risks to third parties, while boundary changing behaviors could alter the potential vulnerabilities of a firm and hence decisions to alter firm boundaries are likely to be shaped by risk perceptions. We further posit that this effect will be shaped by the firm compulsions to avoid, reduce, or take risk. Using data collected for 2015-2018 years from S&P 500 firms by fine-tuning state-of-art transformer-based NLP models with domain specific data and causal extraction techniques to measure organizations’ information security risk perceptions, we find that organizations’ information security risk perception is positively associated with their information security risk hedging behavior, and less-risky boundary changing actions.
Role of Carrier Technology as Innovation Engine: Does Regulation Weaken the Association?

Our research analyzes the role of regulation on innovation. To do so, we bring the carrier technology concept to investigate the impact of regulation on its evident association to innovations. Further, we split innovation into carrier technology-dependent and generic (carrier technology-independent). We performed our analysis using a data set of 140 countries for over three years. Additionally, we support our analysis with an instrumental variable (IV) estimator using the nation's IQ as an instrument. Our empirical findings support a negative impact of regulation on the relationship of carrier technology to carrier technology-dependent innovation; however, we do not find the direct influence on generic innovation, suggesting the varied impact of regulation on innovation. In the end, we describe how our findings equip us with a vision to understand the somewhat neglected but crucial role of regulation, technology, and innovations in development economics.
Does Routine Order Variation and Bot Usage Spur Resilience in Open Source Communities?

Resilience is important for Open Source Software Development (OSSD) teams to overcome software development challenges that commonly occur in the platforms as a result of unexpected events such as natural disasters, cyber-attacks, pandemics etc., As OSSD teams primarily rely on voluntary contributions, any type of disruptions to the routines of open source workers can lead to loss of productivity and cause delays in open software production and release cycles. Currently we do not have sufficient understanding about how OSSD teams bounce back from a disruption and become resilient. To understand the effects of disruption we chose a popular open source community, called Kubernetes, to understand the resilience process in OSSD teams. We collected data from an exemplar event of COVID-19 pandemic to understand how Kubernetes community became resilient after the unprecedented announcement of the global pandemic by World Health Organization (WHO). Overall, we collected over .4 million records corresponding to the activities of 65 Kubernetes teams from March 2020 to May 2020. We use organizational routine lens to understand how routine order variation (the way in which the activities’ order varies) and bot usage can have effects on resilience in open source communities. The results highlight the importance of order variation and bot usage for OSSD teams’ resilience and provide implications for open source project management practises during crisis situations.
Research on target tracking method based on deep reinforcement learning

Recently, the target tracking technology has become a popular scheme, which can assist skaters to achieve better results in training and competition. However, However, this is still a challenging task for smaller, fast-moving targets, such as skaters. To solve above problems, a target tracking method based on the combination of action-decision networks for visual tracking with deep reinforcement learning and the algorithm for precising bounding box estimation, and this method is used for skaters. The proposed method consists of two phases. In the first phases, the proposed method uses a tracker to obtain rough target tracking results, which search area is the position and size of the previous frame tracking result, and the tracker is controlled by sequentially pursuing actions. In the second phases, the alpha-refine, an accurate bounding box estimation algorithm, is applied to precise tracking of the target via the result of the first phases. That is, expand the prediction of the current tracking result to a concentric search area twice the size, and predict a more accurate bounding box in this area as the final tracking result. In addition, to train and test the proposed method, we produced a dataset for skaters. Compared with the traditional target tracking methods, the results of presented dataset show that the proposed method has higher tracking accuracy.
A convolutional neural network based complex scene classification framework using transfer deep combined convolutional activations

In many scene classification applications, the variety of surface objects, high within-category diversity and between-category similarity carry challenges for the classification Framework. Most of CNN-based classification methods only extract image features from a single network layer, which may cause the completed image information difficult to extract in complex scenes. We propose a novel transfer deep combined convolutional activations (TDCCA) to integrate both the low-level and high-level features. Extensive comparative experiments are conducted on UC Merced database, Aerial Image database and NWPU-RESISC45 database. The results reveal that our proposed TDCCA achieves higher experimental accuracies than other up-to-date popular methods.