Reviewerly/exHarmony huggingface dataset:
Quantum systems for Monte Carlo methods and applications to fractional stochastic processes

Random numbers are a fundamental and useful resource in science and engineering with important applications in simulation, machine learning and cyber-security. Quantum systems can produce true random numbers because of the inherent randomness at the core of quantum mechanics. As a consequence, quantum random number generators are an efficient method to generate random numbers on a large scale. We study in this paper the applications of a viable source of unbiased quantum random numbers (QRNs) whose statistical properties can be arbitrarily programmed without the need for any post-processing and that pass all standard randomness tests of the NIST and Dieharder test suites without any randomness extraction. Our method is based on measuring the arrival time of single photons in shaped temporal modes that are tailored with an electro-optical modulator. The advantages of our QRNs are shown via two applications: simulation of a fractional Brownian motion, which is a non-Markovian process, and option pricing under the fractional SABR model where the stochastic volatility process is assumed to be driven by a fractional Brownian motion. The results indicate that using the same number of random units, our QRNs achieve greater accuracy than those produced by standard pseudo-random number generators. Moreover, we demonstrate the advantages of our method via an increase in computational speed, efficiency, and convergence.
CLX: Towards verifiable PBE data transformation.

Effective data analytics on data collected from the real world usually begins with a notoriously expensive pre-processing step of data transformation and wrangling. Programming By Example (PBE) systems have been proposed to automatically infer transformations using simple examples that users provide as hints. However, an important usability issue - verification - limits the effective use of such PBE data transformation systems, since the verification process is often effort-consuming and unreliable. We propose a data transformation paradigm design CLX (pronounced clicks) with a focus on facilitating verification for end users in a PBE-like data transformation. CLX performs pattern clustering in both input and output data, which allows the user to verify at the pattern level, rather than the data instance level, without having to write any regular expressions, thereby significantly reducing user verification effort. Thereafter, CLX automatically generates transformation programs as regular-expression replace operations that are easy for average users to verify. We experimentally compared the CLX prototype with both FlashFill, a state-of-the-art PBE data transformation tool, and Trifacta, an influential system supporting interactive data transformation. The results show improvements over the state of the art tools in saving user verification effort, without loss of efficiency or expressive power. In a user effort study on data sets of various sizes, when the data size grew by a factor of 30, the user verification time required by the CLX prototype grew by 1.3x whereas that required by FlashFill grew by 11.4x. In another test assessing the users' understanding of the transformation logic - a key ingredient in effective verification - CLX users achieved a success rate about twice that of FlashFill users.
Pitfalls and Opportunities in the Development and Evaluation of Artificial Intelligence Systems

We provide a somewhat whimsical and copiously illustrated overview of what we have learned from laboring to curb the enthusiasm of artificial intelligence (AI) algorithm developers with the cold reality of AI systems evaluation. This has become all the more necessary in view of the incredible progress being made in the computational intelligence community using deep learning methods to address real-world problems. Good development practices and reliable evaluation methodology ensure good applications. We stress the receiver operating characteristic (ROC) paradigm, but note the broad range of performance metrics that go beyond the simple calculation of success rate or accuracy on a limited test set. Both parametric and nonparametric uncertainty calculation techniques will be mentioned, with emphasis on bootstrap methodology. We discuss the gamut of conflicting problems that contribute to error and overestimation of validity of modern computational intelligence algorithms and suggest potential solutions to encourage their successful implementation.
Linear response theory of Josephson junction arrays in a microwave cavity

Recent experiments on Josephson junction arrays (JJAs) in microwave cavities have opened up a new avenue for investigating the properties of these devices while minimizing the amount of external noise coming from the measurement apparatus itself. These experiments have already shown promise for probing many-body quantum effects in JJAs. In this work, we develop a general theoretical description of such experiments by deriving a quantum phase model for planar JJAs containing quantized vortices. The dynamical susceptibility of this model is calculated for some simple circuits, and signatures of the injection of additional vortices are identified. The effects of decoherence are considered via a Lindblad master equation.
Impacts and Challenges for Developing Countries

As illustrated in the first four chapters of this book, much of the research investigating the influence of mobile information and communication technology (ICT) on urban mobility systems (including travel behaviors, mobility services, and public policies) focuses, whether explicitly or implicitly, on the mobility systems of developed countries. This chapter draws on a dedicated literature review to analyze the specific opportunities and challenges raised by the spread of smartphones for urban mobility systems in developing countries, which are characterized by rapid changes in travel demand, lack of synchronization with other urban subsystems, and the existence of substantial populations living in poverty.
From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification

Emotion cause identification aims at identifying the potential causes that lead to a certain emotion expression in text. Several techniques including rule based methods and traditional machine learning methods have been proposed to address this problem based on manually designed rules and features. More recently, some deep learning methods have also been applied to this task, with the attempt to automatically capture the causal relationship of emotion and its causes embodied in the text. In this work, we find that in addition to the content of the text, there are another two kinds of information, namely relative position and global labels, that are also very important for emotion cause identification. To integrate such information, we propose a model based on the neural network architecture to encode the three elements ($i.e.$, text content, relative position and global label), in an unified and end-to-end fashion. We introduce a relative position augmented embedding learning algorithm, and transform the task from an independent prediction problem to a reordered prediction problem, where the dynamic global label information is incorporated. Experimental results on a benchmark emotion cause dataset show that our model achieves new state-of-the-art performance and performs significantly better than a number of competitive baselines. Further analysis shows the effectiveness of the relative position augmented embedding learning algorithm and the reordered prediction mechanism with dynamic global labels.
Ontology design based on data family planning field officer using OWL and RDF

Population density in Indonesia is ranked fourth in the world. The impact of a large population will affect the level of welfare of the community to decrease, and the number of unemployment is increasing so that the state makes Family Planning Program (PLKB) to control the rate of population growth. Problems in the PLKB program are on knowledge management and mapping from data contraception, counseling and planning so that this research using Ontology method will aim to do mapping with knowledge management and Ontology design shows represented data to relate and describes the resources contained in family planning data. This research approach the representation of ontology that is validated through model transformation from family planning data to ontology design using OWL and RDF which are useful for data processing and representing data to be utilized by field officers in educating the public and eradicating negative issues about family planning programs
Bayesian Execution Skill Estimation

The performance of agents in many domains with continuous action spaces depends not only on their ability to select good actions to execute, but also on their ability to execute planned actions precisely. This ability, which has been called an agent’s execution skill, is an important characteristic of an agent which can have a significant impact on their success. In this paper, we address the problem of estimating the execution skill of an agent given observations of that agent acting in a domain. Each observation includes the executed action and a description of the state in which the action was executed and the reward received, but notably excludes the action that the agent intended to execute. We previously introduced this problem and demonstrated that estimating an agent’s execution skill is possible under certain conditions. Our previous method focused entirely on the reward that the agent received from executed actions and assumed that the agent was able to select the optimal action for each state. This paper addresses the execution skill estimation problem from an entirely different perspective, focusing instead on the action that was executed. We present a Bayesian framework for reasoning about action observations and show that it is able to outperform previous methods under the same conditions. We also show that the flexibility of this framework allows it to be applied in settings where the previous limiting assumptions are not met. The success of the proposed method is demonstrated experimentally in a toy domain as well as the domain of computational billiards.
Multiple Saliency and Channel Sensitivity Network for Aggregated Convolutional Feature

In this paper, aiming at two key problems of instance-level image retrieval, i.e., the distinctiveness of image representation and the generalization ability of the model, we propose a novel deep architecture - Multiple Saliency and Channel Sensitivity Network(MSCNet). Specifically, to obtain distinctive global descriptors, an attention-based multiple saliency learning is first presented to highlight important details of the image, and then a simple but effective channel sensitivity module based on Gram matrix is designed to boost the channel discrimination and suppress redundant information. Additionally, in contrast to most existing feature aggregation methods, employing pre-trained deep networks, MSCNet can be trained in two modes: the first one is an unsupervised manner with an instance loss, and another is a supervised manner, which combines classification and ranking loss and only relies on very limited training data. Experimental results on several public benchmark datasets, i.e., Oxford buildings, Paris buildings and Holidays, indicate that the proposed MSCNet outperforms the state-of-the-art unsupervised and supervised methods.
Hybrid Reinforcement Learning with Expert State Sequences

Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are unobserved. We propose a novel tensor-based model to infer the unobserved actions of the expert state sequences. The policy of the agent is then optimized via a hybrid objective combining reinforcement learning and imitation learning. We evaluated our hybrid approach on an illustrative domain and Atari games. The empirical results show that (1) the agents are able to leverage state expert sequences to learn faster than pure reinforcement learning baselines, (2) our tensor-based action inference model is advantageous compared to standard deep neural networks in inferring expert actions, and (3) the hybrid policy optimization objective is robust against noise in expert state sequences.
Contrasting Big Bang With Continuous Integration Through Defect Reports

Continuous integration promises ea rlier defect detection, quality improvements, and more customer value delivered faster. In this case study, we examined development of software for the advanced safety and driver support component of a Swedish vehicle manufacturer in two consecutive projects.
Mediação da informação através de plataformas digitais: prática nas bibliotecas arquivos e museus da área metropolitana do porto

The main goal of this study was to investigate how mediation occurs through digital platforms managed by libraries, archives, and museums within the metropolitan area of Porto.This study was applied in the 17 municipalities of the metropolitan area of Porto.This was an exploratory and descriptive research with a qualitative and quantitative approach.Our findings suggest that the increase and improvements of the library and archive services concerning digital technologies need to be revised and the engagement of trained professionals to be responsible for the mediation through digital platforms is required.Therefore, the integration between the digital environment and human resources is necessary for improving the mediation of the information within these services.
A Framework to Coordinate Segmentation and Recognition

A novel coordination framework between the segmentation and the recognition is proposed, to conduct the two tasks collaboratively and iteratively. To accomplish the cooperation, objects are expressed in two aspects: shape and appearance, which are learned and leveraged as constraints to the segmentation so that the object segmentation mask will be consistent with the object regions in the image and the knowledge we have. For the shape, a bottom-top-bottom pathway is built using an encoder-decoder network with capsule neurons, where the encoder extracts the features of the shape that used for recognition and the decoder generates reference shapes according to these features and the recognition result. During this procedure, capsule neurons can parse the existence of the object and cope with the interference in the segmentation. The appearance knowledge is utilized in another pathway to assist the segmentation processing. Both the shape and appearance information are dependent on the recognition result, thus allowing the classifier to convey object information to the segmenter. Experiments demonstrate the effectiveness of our framework and model in collaboratively segmenting and recognizing objects that can be recognized using their shapes/shape-patterns.
Deep Meditations: Controlled navigation of latent space

We introduce a method which allows users to creatively explore and navigate the vast latent spaces of deep generative models. Specifically, our method enables users to \textit{discover} and \textit{design} \textit{trajectories} in these high dimensional spaces, to construct stories, and produce time-based media such as videos---\textit{with meaningful control over narrative}. Our goal is to encourage and aid the use of deep generative models as a medium for creative expression and story telling with meaningful human control. Our method is analogous to traditional video production pipelines in that we use a conventional non-linear video editor with proxy clips, and conform with arrays of latent space vectors. Examples can be seen at \url{http://deepmeditations.ai}.
How do Individuals Interpret Multiple Conceptual Models? A Theory of Combined Ontological Completeness and Overlap

When analyzing or designing information systems, users often work with multiple conceptual models because each model articulates a different, partial aspect of a real-world domain. However, the available research in this area has largely studied the use of single modeling artifacts only. We develop a new theory about interpreting multiple conceptual models that details propositions for evaluating how individuals select, understand, and perceive the usefulness of multiple conceptual models. We detail implications of our theory development for empirical research on conceptual modeling. We also outline practical contributions for the design of conceptual models and for choosing models for systems analysis and design tasks. Finally, to stimulate research that builds on our theory, we illustrate procedures for enacting our theory and discuss a range of empirically relevant boundary conditions
Biometric Image Encryption Algorithm Based on Modified Rubik’s Cube Principle

Multi-biometric systems are being mostly installed in many large-scale biometric applications most importantly UIDAI system in India. However, multi-biometric systems require storage of multiple biometric templates for each user that results in increased risk to user privacy and cybercrimes. Among all the biometrics, fingerprints are widely used in personal verification systems especially from small-scale to large-scale organization. Encryption of the biometric images is very important due to increasingly use of insecure systems and networks for storage, transmission, and verification. We proposed encryption algorithm based on modified Rubik’s cube principle for fingerprint biometric images in this paper. Secret key for the encryption is obtained from the same fingerprint biometric image that assists in key management and storage for further decryption and verification process. The principle of Rubik’s cube involves row and column rotation or changing of the pixels position. Further, bitwise XOR operation through modifications in secret key adds to security of the biometric images. Experimental simulations were performed to obtain the encryption parameters and validate the algorithm through common attacks such as noise and compression. Thus, the proposed encryption algorithm may be applied to fingerprint images used in attendance monitoring systems since it provides key management and does not involve complex calculations.
A Comprehensive Study on Character Segmentation

In identifying the characters from a given image, character segmentation plays an important role. In a given line of text, first, we have to segment the words. Then, in each word there will be a character-by-character segmentation. There have been some rapid developments in this area. Many algorithms have been implemented to increase the accuracy range and decrease the word error rate. This paper aims to provide a review of some of the developments that have happened in this domain.
Energy Expenditure Calculation with Physical Activity Recognition Using Genetic Algorithms

Physical health is associated with physical activity, physical activity also ensures the wellbeing of the humans, physical activity is recognized using body worn sensors, and three Inertial Measurement units (IMU) are used to capture the data from the sensors. The activity recognition chain consists of Data Acquisition, Preprocessing, segmentation, Feature extraction, and Classification. Different levels of research are carried out on each stage. In feature selection genetic algorithms are used but the paper proposing the memetic algorithms an enhanced version of the genetic algorithms with local search in the each stage of genetic algorithm. This technique shall eliminate the chances of energy loss and consequently increase efficiency of the current system.
Honoring volunteers and sharing knowledge on the SEG Wiki

The SEG Wiki serves as a key foundation of the Society to engage the membership, the geoscience community, and the public in all-things applied geophysics. The wiki's main mission is to supply scientific material to the geoscience community and the public through online books, geophysical tutorials, geoscience articles, and biographies of geoscientists. SEG's two best-selling books are provided open and free of charge on the wiki. They include the Encyclopedic Dictionary of Applied Geophysics by Robert E. Sheriff and Seismic Data Analysis by Öz Yilmaz. A third book, Problems in Exploration Seismology and Their Solutions by L. P. Geldart and Robert E. Sheriff, will be added soon.
MENINGKATKAN KEMAMPUAN PRA-MATEMATIKA DENGAN MENGGUNAKAN MEDIA BALOK CUISENAIRE PADA ANAK KELOMPOK A2 PAUD HAQIQI KOTA BENGKULU

This study aied to improve the pre-mathematics ability through the introduction to pattern activity and classification by using Cuisenaire block media on children of group A2 at Early Childhood Education Program (PAUD) of Haqiqi of Bengkulu City. The research method used in this study was Classroom Action Research (CAR) which was conducted through two cycles and each cycle consisted on three meetings. The subject of the study were as many as 9 students consisted of 5 male student and 4 female students. Data collection technique was done by using observation method. Data analysis was conducted by calculating the average score and learning mastery score. The result of the study revealed that there was improvement on pre-mathematic ability of the children through the introduction to pattern activity and classification with the average score of 4,3 and learning mastery of 86% categorized as very good criteria. It is suggested for the next research to use cuisenaire block as a media to measure other abilities with several aspect that need to be considered such as size and basic pattern.
What is illegal offline is also illegal online: the German Network Enforcement Act 2017

Constitutional democracies traditionally entrust courts rather than administrative agencies with the regulation of speech. Proposals to involve bureaucrats in speech regulation are quickly dismissed as government censorship. However, with the massive proliferation of harmful speech on the Internet, the judicial system is less and less capable to enforce legal standards online. Against this backdrop, a debate has emerged on whether or not to authorize an administrative agency with the online enforcement of free speech principles as defined by constitutional law and interpreted by the courts. In 2017, the German legislator led this idea to fruition and enacted the “Network Enforcement Law”, which gives the federal bureaucracy the power to evaluate and assess the internal speech policies of major online platforms. This paper analyzes the Enforcement Act and asks whether the new law adequately distributes the responsibility for preventing harmful speech between three major stakeholders: intermediaries, the administration, and courts. While the German law has its weaknesses, the paper argues that its general approach is overall convincing.