Reviewerly/exHarmony huggingface dataset:
Non-Markovianity of qubit evolution under the action of spin environment

The question, whether an open system dynamics is Markovian or non-Markovian can be answered by studying the direction of the information flow in the dynamics. In Markovian dynamics, information must always flow from the system to the environment. If the environment is interacting with only one of the subsystems of a bipartite system, the dynamics of the entanglement in the bipartite system can be used to identify the direction of information flow. Here we study the dynamics of a two-level system interacting with an environment, which is also a heat bath, and consists of a large number of two-level quantum systems. Our model can be seen as a close approximation to the 'spin bath' model at low temperatures. We analyze the Markovian nature of the dynamics, as we change the coupling between the system and the environment. We find the Kraus operators of the dynamics for certain classes of couplings. We show that any form of time-independent or time-polynomial coupling gives rise to non-Markovianity. Also, we witness non-Markovianity for certain parameter values of time-exponential coupling. Moreover, we study the transition from non-Markovian to Markovian dynamics as we change the value of coupling strength.
Ecosystem development

The precursors to the development of a digital ecosystem based around cryptographic consensus technology (CCTech) are considered. The nature of an ecosystem and the relationship of pioneer and secondary species is examined in relation to financial and non-financial iterations of the technology, in particular how this interacts with the current financial narrative. The role of institutional arrangements and supporting legal conditions is discussed. Developments at different technical, societal and regulatory levels of the technology are relevant to ecosystem development. Carlotta Perez’s characterization of the irruption of new technologies provides useful insights to the facilitating, and potentially inhibiting, influence of financial iterations of CCTech on the ecosystem. Whether the existing regulatory approach has hampered ecosystem development is queried.
FitEllipsoid: a fast supervised ellipsoid segmentation plugin

The segmentation of a 3D image is a task that can hardly be automatized in certain situations, notably when the contrast is low and/or the distance between elements is small. The existing supervised methods require a high amount of user input, e.g. delineating the domain in all planar sections. We present FitEllipsoid, a supervised segmentation code that allows fitting ellipsoids to 3D images with a minimal amount of interactions: the user clicks on a few points on the boundary of the object on 3 orthogonal views. The quantitative geometric results of the segmentation of ellipsoids can be exported as a csv file or as a binary image. The core of the code is based on an original computational approach to fit ellipsoids to point clouds in an affine invariant manner. The plugin is validated by segmenting a large number of 3D nuclei in tumor spheroids, allowing to analyze the distribution of their shapes. User experiments show that large collections of nuclei can be segmented with a high accuracy much faster than with more traditional 2D slice by slice delineation approaches. We designed a user-friendly software FitEllipsoid allowing to segment hundreds of ellipsoidal shapes in a supervised manner. It may be used directly to analyze biological samples, or to generate segmentation databases necessary to train learning algorithms. The algorithm is distributed as an open-source plugin to be used within the image analysis software Icy. We also provide a Matlab toolbox available with GitHub.
Three-level learning for improving cross-project logging prediction for if-blocks

Log statements present in the source code provide important execution information to the software developers while debugging. Predicting source code constructs that must be logged is a technically challenging task. Machine learning models are popularly used in literature for logging prediction. These models may not be suitable for small or new projects as these projects do not have sufficient prior data to train the prediction model. For such scenarios, cross-project logging prediction can be useful, in which knowledge gained from the standard project(s) is used to build the model. This paper proposes a three-level model, CLIF, for Cross-project if-blocks logging prediction. CLIF first converts textual features into numeric-textual features(Level-1). CLIF then combines numeric-textual features with Boolean and numeric features(Level-2). Since only few code constructs are logged, CLIF finally learns an effective imbalance decision threshold boundary to predict logged/non-logged code constructs(Level-3). We use 2 text mining classifiers(Level-1) and 8 machine learning classifiers(Level-2) and generate a total of 16 CLIF classifiers. We evaluate performance of CLIF classifiers on three open source projects(six source & target project pair). Experimental results show that CLIF outperforms the existing LogOpt-Plus model and give an improvement of up to 8.21% in average F1-score, and 4.89% in average ROC-AUC.
The graph tessellation cover number: Chromatic bounds, efficient algorithms and hardness

A tessellation of a graph is a partition of its vertices into vertex disjoint cliques. A tessellation cover of a graph is a set of tessellations that covers all of its edges. The $t$-tessellability problem aims to decide whether there is a tessellation cover of the graph with $t$ tessellations. This problem is motivated by its applications to quantum walk models, in especial, the evolution operator of the staggered model is obtained from a graph tessellation cover. We establish upper bounds on the tessellation cover number given by the minimum between the chromatic index of the graph and the chromatic number of its clique graph and we show graph classes for which these bounds are tight. We prove $\mathcal{NP}$-completeness for $t$-tessellability if the instance is restricted to planar graphs, chordal (2,1)-graphs, (1,2)-graphs, diamond-free graphs with diameter five, or for any fixed $t$ at least 3. On the other hand, we improve the complexity for 2-tessellability to a linear-time algorithm.
Composite Sequential Modeling for Identifying Fake Reviews

Abstract This paper presents a comprehensive analysis and comparison of various proposed sequential models based on different deep networks such as the convolutional neural network, long short-term memory, and recurrent neural network. The different sequential models are analyzed based on the number of layers, the number of output dimensions, order, and the combination of different deep network architectures. The proposed approach is compared to a baseline model based on traditional machine learning techniques.
Generalization and Improvement of the Levenshtein Lower Bound for Aperiodic Correlation

This paper deals with lower bounds on aperiodic correlation of sequences. It intends to solve two open questions. The first one is on the validity of the Levenshtein bound for a set of sequences other than binary sequences or those over the roots of unity. Although this result could be a priori extended to polyphase sequences, a formal demonstration is presented here, proving that it does actually hold for these sequences. The second open question is on the possibility to find a bound tighter than Welch's, in the case of a set consisting of two sequences M = 2. By including the specific structure of correlation sequences, a tighter lower bound is introduced for this case. Besides, this method also provides in the cases M = 3 and M = 4 a tighter bound than the up-to-now tightest bound provided by Liu et al.
CAUGHT IN THE NET. NOTES FROM THE ELECTRONIC UNDERGROUND

Reflections on the epistemological and institutional shifts in scholarly research and publication that are brought about by electronic technology. Particular attention is paid to the potential crisis in current modes of the legitimation of knowledge and scholarly practices.
SolarView: Low Distortion Radial Embedding with a Focus

We propose a novel type of low distortion radial embedding which focuses on one specific entity and its closest neighbors. Our embedding preserves near-exact distances to the focus entity and aims to minimize distortion between the other entities. We present an interactive exploration tool SolarView which places the focus entity at the center of a “solar system” and embeds its neighbors guided by concentric circles. SolarView provides an implementation of our novel embedding and several state-of-the-art dimensionality reduction and embedding techniques, which we adapted to our setting in various ways. We experimentally evaluated our embedding and compared it to these state-of-the-art techniques. The results show that our embedding competes with these techniques and achieves low distortion in practice. Our method performs particularly well when the visualization, and hence the embedding, adheres to the solar system design principle of our application. Nonetheless-as with all dimensionality reduction techniques-the distortion may be high. We leverage interaction techniques to give clear visual cues that allow users to accurately judge distortion. We illustrate the use of SolarView by exploring the high-dimensional metric space of bibliographic entity similarities.
Ontology-Based Modeling of Effect-Based Knowledge in Disaster Response

Emergency response and management requires the coordination of agencies and different services in a complex evolving situation. This in turn, requires diverse models representing detailed knowledge about the types of adverse events, their potential impact and the means and resources that are best suited for an effective response. The basic formal infrastructure incident assessment ontology (BFiaO) is oriented towards fulfilling these needs. BFiaO is a meta-model for handling infrastructure-related situations, but it did not provide models for a catalogue of adverse events and the means necessary for an adequate response. In this article, the authors present the key ontological commitments required for developing BFiaO-based extensible typologies of adverse events that are driven by the effects rather than by other aspects such as causes, or facilities affected. The model of a concrete case study is then presented that connects adverse event types to the kind of actions and resources required for mitigation.
The mediating effect of employee commitment on recruitment process towards organizational performance in UAE organizations

The primary objective of this paper is to investigate the impact of the recruitment process on the organizational performance.It has been hypothesized in this study that an effective recruitment process could enhance the organizational process.After critically reviewing the literature, the study finds employee commitment as a key factor which affects the relationship between recruitment process and organizational performance.Therefore the second objective of the current study is to investigate the mediating role of employee commitment in the relationship between recruitment process and organizational performance.The data is collected from 247 employees of Fujairah National Group of UAE and the structural equation modeling using AMOS is used to analyze the data.The findings of the study show a great deal of agreement with the results.The findings of the study will be helpful for HR practitioners, policymakers and researchers in understanding the relationship between employee commitment, recruitment process, and organizational performance.
An Empirical Study Towards Understanding How Deep Convolutional Nets Recognize Falls

Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone. In recent years, deep convolutional nets are widely used in human action analysis, based on which a number of fall detection methods have been proposed. Despite their highly effective performances, the behaviors of how the convolutional nets recognize falls are still not clear. In this paper, instead of proposing a novel approach, we perform a systematical empirical study, attempting to investigate the underlying fall recognition process. We propose four tasks to investigate, which involve five types of input modalities, seven net instances and different training samples. The obtained quantitative and qualitative results reveal the patterns that the nets tend to learn, and several factors that can heavily influence the performances on fall recognition. We expect that our conclusions are favorable to proposing better deep learning solutions to fall detection systems.
A Generalized Idiom Usage Recognition Model Based on Semantic Compatibility

Many idiomatic expressions can be used figuratively or literally depending on the context. A particular challenge of automatic idiom usage recognition is that idioms, by their very nature, are idiosyncratic in their usages; therefore, most previous work on idiom usage recognition mainly adopted a “per idiom” classifier approach, i.e., a classifier needs to be trained separately for each idiomatic expression of interest, often with the aid of annotated training examples. This paper presents a transferred learning approach for developing a generalized model to recognize whether an idiom is used figuratively or literally. Our work is based on the observation that most idioms, when taken literally, would be somehow semantically at odds with their context. Therefore, a quantified notion of semantic compatibility may help to discern the intended usage for any arbitrary idiom. We propose a novel semantic compatibility model by adapting the training of a Continuous Bag-of-Words (CBOW) model for the purpose of idiom usage recognition. There is no need to annotate idiom usage examples for training. We perform evaluative experiments on two corpora; results show that the proposed generalized model achieves competitive results compared to state of-the-art per-idiom models.
Lightweight Distributed Suffix Array Construction

We present two new distributed suffix array construction algorithms. One of our algorithms requires only half as much memory as its competitor (PSAC) [Flick & Aluru, SC 2015], while achieving similar speed. In practice, we can compute on the same hardware suffix arrays for text twice as large as PSAC. The other algorithm still requires less memory than PSAC but is faster on some instances. As a by-product, we also engineered the first distributed string sorting algorithm. All of our algorithms are tested on text collections of up to 115 GB and running on 1280 cores.
Automated Video Face Labelling for Films and TV Material

The objective of this work is automatic labelling of characters in TV video and movies, given weak supervisory information provided by an aligned transcript. We make five contributions: (i) a new strategy for obtaining stronger supervisory information from aligned transcripts; (ii) an explicit model for classifying background characters, based on their face-tracks; (iii) employing new ConvNet based face features, and (iv) a novel approach for labelling all face tracks jointly using linear programming. Each of these contributions delivers a boost in performance, and we demonstrate this on standard benchmarks using tracks provided by authors of prior work. As a fifth contribution, we also investigate the generalisation and strength of the features and classifiers by applying them “in the raw” on new video material where no supervisory information is used. In particular, to provide high quality tracks on those material, we propose efficient track classifiers to remove false positive tracks by the face tracker. Overall we achieve a dramatic improvement over the state of the art on both TV series and film datasets, and almost saturate performance on some benchmarks.
Using a multi-objective optimal design of GMDH type neural networks to evaluate the quality of treated water in a water treatment plant

In this research, a model based on the multi-objective optimal design of GMDH 1 -type neural network is proposed for evaluating the quality of treated water.To validate the proposed model, a case study was carried out based on the data sets obtained from Rasht Water Treatment Plant (WTP), Guilan, Iran.For modeling, the experimental data obtained from the laboratory and operation unit were divided into training and testing groups (70% for training and 30% for testing).After modeling, the predicted values were compared with the ones obtained from the experimental values.The determination coefficient of the predicted values for the two data sets of GMDH model (laboratory and operation unit) were 0.9905 and 0.9714 respectively.Comparison between experimental and mathematical results from GMDH-type neural networks showed the success of this method.
A Novel Based On Image Blocking Method To Encrypt-Decrypt Color

Encryption of digital color image is the process of conversion original digital color image into an encrypted one to protect the image from hacking or to prevent an authorized person to get the valuable information located in the color image. The process of color image encryption-decryption is very important issue in human activities and here in this paper we will introduce a new simple, efficient and highly secure method to be used for color image encryption-decryption. The proposed method will be tested and implemented, the efficiency parameters of the proposed method will be calculated and will be compared with other methods parameters to prove the efficiency issues of the proposed method.
Feedback from Operations to Software Development—A DevOps Perspective on Runtime Metrics and Logs

DevOps achieve synergy between software development and operations engineers. This synergy can only happen if the right culture is in place to foster communication between these roles. We investigate the relationship between runtime data generated during production and how this data can be used as feedback in the software development process. For that, we want to discuss case study organizations that have different needs on their operations-to-development feedback pipeline, from which we abstract and propose a more general, higher-level feedback process. Given such a process, we discuss a technical environment required to support this process. We sketch out different scenarios in which feedback is useful in different phases of the software development life-cycle.
Exploring and Expanding GSE Education with Open Source Software Development

Global software engineering (GSE) courses traditionally require cooperation between at least two universities so as to provide a distributed development environment to the students. In this study, we explore an alternative way to organize a global software engineering course where students work on open source software development (OSSD) projects rather than in a multi-university collaboration setting. The results show that the new setup may provide core GSE challenges as well as challenges associated with software development outsourcing and challenges related to working on large open source software. The present article compares the experiences gained from running a combined GSE and OSSD course against the experiences gained from running a traditional GSE course. The two alternatives are compared in terms of students’ learning outcomes and course organization. The authors found that a combined GSE and OSSD course provides learning opportunities that are partly overlapping with, and partly complementary to, a traditional GSE course. The authors also found that the combined OSSD and GSE course was somewhat easier to organize because most of the activities took place in a single university setting. The authors used the extended GSE taxonomy for the comparison and found it to be a useful tool for this, although it had some limitations in expressive power. Therefore, two additional relationship dimensions are proposed that will further enrich the extended taxonomy in classifying GSE (and OSSD) projects.
Variants of the AES Key Schedule for Better Truncated Differential Bounds

Differential attacks are one of the main ways to attack block ciphers. Hence, we need to evaluate the security of a given block cipher against these attacks. One way to do so is to determine the minimal number of active S-boxes, and use this number along with the maximal differential probability of the S-box to determine the minimal probability of any differential characteristic. Thus, if one wants to build a new block cipher, one should try to maximize the minimal number of active S-boxes. On the other hand, the related-key security model is now quite important, hence, we also need to study the security of block ciphers in this model. In this work, we search how one could design a key schedule to maximize the number of active S-boxes in the related-key model. However, we also want this key schedule to be efficient, and therefore choose to only consider permutations. Our target is AES, and along with a few generic results about the best reachable bounds, we found a permutation to replace the original key schedule that reaches a minimal number of active S-boxes of 20 over 6 rounds, while no differential characteristic with a probability larger than $$2^{-128}$$ exists. We also describe an algorithm which helped us to show that there is no permutation that can reach 18 or more active S-boxes in 5 rounds. Finally, we give several pairs $$(P_s, P_k)$$ , replacing respectively the ShiftRows operation and the key schedule of the AES, reaching a minimum of 21 active S-boxes over 6 rounds, while again, there is no differential characteristic with a probability larger than $$2^{-128}$$ .
Quantification of coffinite (USiO4) in roll-front uranium deposits using visible to near infrared (Vis-NIR) portable field spectroscopy

Coffinite (USiO4) is a common uranium-bearing mineral of roll-front uranium deposits. This mineral can be identified by the visible near infrared (Vis-NIR) portable field spectrometers used in mining exploration. However, due to the low detection limits and associated errors, the quantification of coffinite abundance in the mineralized sandstones or sandy sediments of roll-front uranium deposits using Vis-NIR spectrometry requires a specific methodological development. In this study, the 1135 nm absorption band area is used to quantify the abundance of coffinite. This absorption feature does not interfere with NIR absorption bands of any other minerals present in natural sands or sandstones of uranium roll-front deposits. The correlation between the 1135 nm band area and coffinite content was determined from a series of spectra measured from prepared mineral mixtures. The samples were prepared with a range of weighted amounts of arenitic sands and synthetic coffinite simulating the range of uranium concentration encountered in roll-front uranium deposits. The methodology presented in this study provides the quantification of the coffinite content present in sands between 0.03 wt% to 1 wt% coffinite with a detection limit as low as 0.005 wt%. The integrated area of the 1135 nm band is positively correlated with the coffinite content of the sand in this range, showing that the method is efficient to quantify coffinite concentrations typical of roll-front uranium deposits. The regression equation defined in this study was then used as a reference to predict the amount of natural coffinite in a set of mineralized samples from the Tortkuduk uranium roll-front deposit (South Kazakhstan).