Reviewerly/exHarmony huggingface dataset:
Isomorphism Theorems for Coalgebras

Abstract Let F be an endofunctor of a category C . We prove isomorphism theorems for F -coalgebras under condition that the underlying category C is exact; that is, regular with exact sequences. Also, F is not assumed to preserve pullbacks.
Motion induced error compensation method for digital fringe projection system

Digital fringe projection technique is widely used for high-accuracy three-dimensional (3D) shape measurements. However, when the measured object is moving, there will be severe artifacts and phase errors induced by the movement even though a high-speed fringe projection system is used. Meanwhile, there are different kinds of motion such as uniform and non-uniform motion, which makes the problem even harder to address. To this end, this paper proposes a generic motion error compensation algorithm to deal with different motion artifacts. Both simulation and experiments demonstrated the proposed method can substantially reduce motion-induced measurement error.
Proof analysis and rules creation on DNA expert opinions

It is a new angle of view from that of logic, experience and science to construct the proof analysis theory and research method in the present evidence law research, which helps to broaden the research field of science of evidence law. It is necessary to conduct thorough proof analysis of DNA appraisement opinions from the five aspects of object of proof, logic derivation relation, incidence relation, incidence strength, and credibility. Analysis of the proof object can make the domain of judicial proof of DNA appraisement opinions clear. Analysis of the logic derivation relation can make the proof principles of DNA appraisement opinions clear. Analysis of the incidence relation can make the proof nature of DNA appraisement opinions clear. Analysis of the incidence strength can make the proof function of DNA appraisement opinions clear. Analysis of the credibility can find all kinds of factors which influence judicial application, including aspects of authenticity, accuracy and reliability. In order to control the judicial application of DNA appraisement opinions from the angle of proof, it is necessary to establish the notion of optimization of proof and the notion of systematic incidence, and create the new rules of the double-blind appraisement rule, the synchronous dual appraisement rule and the absolute exclusionary rule.
Arithmetic Garbling from Bilinear Maps

We consider the problem of garbling arithmetic circuits and present a garbling scheme for inner-product predicates over exponentially large fields. Our construction stems from a generic transformation from predicate encryption which makes only blackbox calls to the underlying primitive. The resulting garbling scheme has practical efficiency and can be used as a garbling gadget to securely compute common arithmetic subroutines. We also show that inner-product predicates are complete by generically bootstrapping our construction to arithmetic garbling for polynomial-size circuits, albeit with a loss of concrete efficiency. In the process of instantiating our construction we propose two new predicate encryption schemes, which might be of independent interest. More specifically, we construct (i) the first pairing-free (weakly) attribute-hiding non-zero inner-product predicate encryption scheme, and (ii) a key-homomorphic encryption scheme for linear functions from bilinear maps. Both schemes feature constant-size keys and practical efficiency.
Color Features Extraction and Classification of Digital Images of Erythrocytes Infected by Plasmodium berghei

The development of antimalarial drugs requires performing laboratory experiments that include the analysis of blood smears infected with Plasmodium berghei. Analyzing visually the resulting microscopy images is usually a slow and tedious task prone to errors due to fatigue and subjectivity of the analysts. These facts motivated the creation of digital image processing systems to automate the aforementioned analysis. We present in this work a computer vision solution which processes microscopy images of blood smears. This system performs tasks like illumination correction, color compensation, image segmentation including separation of clumped objects and the extraction and selection of color features. Then a set of classifiers was tested to find the best one in terms of classification results. Here a new feature named pixels fraction was introduced and a number of other color features were extracted, from which a subset was selected for the classification of the cells into either normal or infected. The classifiers tested for this application were: support vector machines (SVM), K-nearest neighbors (KNN), J48, Random Forest (RF), Naïve Bayes and linear discriminant analysis (LDA). All of them were evaluated in terms of their performance expressed as correct classification rate, sensitivity, specificity, F-measure and area under Receiver Operating Characteristic (ROC) curve (AUC). The usefulness of the pixels fraction as a new and effective feature was demonstrated by the experimental results. In regard of classifiers, J48 and Random Forest showed the best results.
The Effect of Organizational Culture and Community Perceived by Elementary School Teachers on Relationship Satisfaction

The purpose of this study is to investigate the influence of organization culture and educational community on teachers" perceived satisfaction with members like directors, young children, parents, and peer teachers. The subjects consisted of 148 elementary school teachers working in the P city and data were collected and analyzed by means of descriptive statistics, correlation, and multiple regression. As a result, teachers tend to perceive subcultures of school culture and educational culture as above average. And there were significant correlations between variables of organizational culture and relational satisfactions with members(r =.16~.88), between educational communality of the school and relational satisfactions with members(r =.58~.84). As a result of Regression analysis total explanation of the independent variables, excepting Rational culture on relational satisfactions with members was 92% and there were differences in the explanations in case of each members. Based on this, we discussed the leadership of the principal and the improvement of the sharing of core values between the organizational culture and the members of the school.
Two-tier Blockchain Timestamped Notarization with Incremental Security

Digital notarization is one of the most promising services offered by modern blockchain-based solutions. We present a digital notary design with incremental security and cost reduced with respect to current solutions. A client of the service receives evidence in three steps. In the first step, evidence is received almost immediately, but a lot of trust is required. In the second step, less trust is required, but evidence is received seconds later. Finally, in the third step evidence is received within minutes via a public blockchain.
Partitioning of an arbitrary domain into subdomains without branching of inner boundaries

In this paper we consider an approach to the partitioning of a arbitrary domain into connected subdomains without branching of internal boundaries. The Reeb graph is a simplified representation of the topology of the required domain. Algorithms are shown in this paper is a modification of the presented earlier algorithm of the formation of the plane Reeb graph. The algorithm for constructing the volume Reeb graph allows us to determine the internal topology of the domain. A new algorithm of the formation of the partitioning into subdomains without branching of internal boundaries is presented.
Multiphoton quantum technologies: multiphoton state manipulation and characterization

The control of photons represents a pillar for our modern technological society. The emerging field of quantum photonics exploits quantum properties of light to dramatically improve the performance of protocols for metrology, communication and information processing. However, modern applications exploit very little of the enormous potential of the photon. Unfortunately, the challenges involved in the preparation and characterization of photonic states with multiple particles, impose practical limitations to realistic quantum technologies. In this talk, I will report on our recent results on the preparation, manipulation and characterization of quantum states with multiple photons. I will describe how the manipulation of the quantum electromagnetic fluctuations of a pair of vacuum states leads to a novel family of quantum-correlated multiphoton states with tunable mean photon numbers and degrees of correlations. Our technique relies on the use of conditional measurements to engineer the vacuum and consequently the excitation mode of the field through the simultaneous subtraction of photons from two-mode squeezed vacuum states. In addition, I will describe the potential of combing these states with photon number resolving measurements for quantum phase estimation. The last part of my talk will be devoted to discuss our recent technique that allowed us to utilize compressive sensing to demonstrate measurement of high-dimensional states, that describe telecom photon pairs in the spatial and spectral degrees of freedom, with 12 billions of elements using only a small fraction of measurements.
A preliminary study on the use of emotional recurrence analysis to identify coordination in collaborative learning

The use of intelligent tutoring systems to support coordination in collaborative distant learning has become an important topic. This study investigated how the coupling of emotional states detected from facial expressions can be used as indices. Forty participants engaged in a simple explanation task that required coordination; their facial expressions were recorded. We analyzed the learners' emotional recurrence and investigated its validity through comparisons with randomized variables of emotion. Furthermore, we investigated the extent to which variables can be used as indices to determine learning performance. Based on these results, we discuss how to further investigate the use of emotional recurrence to predict collaborative learning for future work.
Math Formula Script and Type Identification and Recognition

In this work, we propose a system for math formula script and type identification based on Convolutional Neural Network, to automatically discriminate between Printed/Handwritten and Arabic/Latin formulas before their recognition by the appropriate recognizer. An identification rate of 94.6% is reached, tested on 320 formulas. For formula recognition, we focused on Arabic machine-printed formulas and we proposed a syntax-directed system, based on symbols recognition and their arrangement analysis. To recognize symbols, we combined some statistical features and a Bayes network classifier. A rate of 96.56% for symbol recognition is achieved. For formula structure analysis, the system proceeds by top-down and bottom-up parsing scheme based on operator dominance. A set of replacement rules is defined. Formula parsing consists in applying, from the dominant operator and its context, the appropriate rule to divide the formulas into sub-formulas which will be recursively analyzed by the same way. The parser used for the formula structure analysis has shown its efficiency with a recognition rate 97.63%.
Automated detection of microearthquakes in continuous noisy records produced by local ocean bottom seismographs or coastal networks

Records of seismic waves produced by local ocean bottom seismographs (OBS) or coastal networks are noisy.The noises, such as high-amplitude microquakes, different anthropogenic noises related to engineering works, explosions, land and sea transport noises, microseisms, or the high-frequency ambient noise, complicate the detection of earthquakes and, in particular, microearthquakes in continuous records produced by local seismic networks.Here we present an algorithm for the automated detection of useful signals in seismic records contaminated by noises.Typical useful and noisy signals at offshore and coastal sites are described together with a brief analysis of the application of the well-known methods to detect useful signals.The proposed algorithm makes a use of the following observations: (i) an increase in the signal amplitude, (ii) consistency over different seismic stations, and (iii) the signal duration.The cumulative short-term-average and long-term-average envelope function is used to estimate the signal duration and to distinguish useful seismic signals from short high-amplitude noisy microquakes.The proposed algorithm was tested on the records produced by local network in the north Egypt coast zone during engineering seismological studies and revealed its effectiveness.
Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual. Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings. Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed.
Fast and Accurate Person Re-identification with Xception Conv-Net and C2F

Person re-identification (re-id) is the task of identifying a person of interest across disjoint camera views in a multi-camera system. This is a challenging problem due to the different poses, viewpoints and lighting conditions. Deeply learned systems have become prevalent in the person re-identification field as they are capable to deal with the these obstacles. Conv-Net using a coarse-to-fine search framework (Conv-Net+C2F) is such a deeply learned system, which has been developed with both a high-retrieval accuracy as a fast query time in mind. We propose three contributions to improve Conv-Net+C2F: (1) training with an improved optimizer, (2) constructing Conv-Net using a different Convolutional Neural Network (CNN) not yet used for person re-id and (3) coarse descriptors having fewer dimensions for improved speed as well as increased accuracy. With these adaptations Xception Conv-Net+C2F achieves state-of-the-art results on Market-1501 (single-query, 72.4% mAP) and the new, challenging data split of CUHK03 (detected, 42.6% mAP).
A Novel Multi-purpose Deep Architecture for Facial Attribute and Emotion Understanding

Facial expression estimation has for years been studied benefiting a wide array of application areas ranging from information retrieval and sentiment analysis to video surveillance and emotion analysis. Methods have been proposed to tackle the problem of facial attribute recognition using deep architectures yielding high accuracies, however less efforts exist to focus on the performance of these architectures. Here in this work, we make use of Squeeze-Net [6] for the first time in the literature to perform facial emotion recognition benchmarked on Celeb-A and AffectNet datasets. Here we extend Squeeze-Net by introducing a new 5 $$\times $$ 5 convolution kernel after the last fully-connected layer offered by Squeeze-Net, merging the 1 $$\times $$ 1 and 3 $$\times $$ 3 outputs from the last fully-connected layers, to perform a more domain-specific feature extraction. We run extensive experiments using widely-used datasets; i.e. Celeb-A and AffectNet, using AlexNet and Squeeze-Net in addition to our proposed architecture. Our proposed architecture, an extension to Squeeze-Net, yields results inline with state of the art while offering a simple architecture involving less complexity compared to state of the art, reporting accuracies of 90.47% and 56.38% compared to 90.94% and 52.36%, in Attribute Prediction and Expression Prediction respectively.
PAC-PAC

Point and click games are a more interactive and engaging alternative to usual photo galleries and video players for promoting tourist locations on the web. The PAC-PAC project aims at supporting users without programming knowledge in creating such games starting from real photos and videos. In this paper, we discuss an intelligent support and the related user interface for supporting them in developing the game story. The user selects a set of photos and a deep-learning based generator returns a story draft for each scene. The user can regenerate the story controlling some net parameters or tailor the text to his or her own tastes.
Evolutionary Image Thresholding for Image Segmentation

Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hybrid bacteria foraging optimization technique and particle swam optimization (hBFOA-PSO). The proposed hBFOA-PSO algorithm performance in segmenting the image is tested using natural and standard images. Experiments show that the proposed hBFOA-PSO is better than particle swarm optimization (PSO), the cuckoo search (CS) and the adaptive Cuckoo Search (ACS).
Virtual Reality Applications in the Extractive Industry—A Short Review

The mining industry presents itself as one of the sectors with major occupational accidents, being only surpassed by agriculture (including fishing) and forestry (including hunting). The technology development has been improving safety awareness and working practises; one of the most used tools is virtual reality (VR). The PRISMA Statement methodology was applied in order to find evidence of these new technologies in the extractive industry. For that, a set of keywords related to both topics (mining and VR) was defined, and the main journals and databases in the engineering field were screened. After applied both exclusion and inclusion criteria, the 1786 papers were reduced to eight. The results show that this resource is useful, however, when comparing to the application in the construction field, in what concerns to safety management, there is still a lot to achieve.
Context of the Person in Social Networks

In this chapter, the authors define the context of the person in social networks. Subsequently they introduce modeling and context of the development process of a person. Then they work on analyzing and feeling defined analysis technique, the sour feelings tweets and their characteristics, and method of how to recuperate data from Twitter by using API extraction.
Clustering Algorithms for Image Segmentation

Depending on the implementation sometimes it is necessary to work directly on the pixels domain. When it occurs the clustering, methods are good alternatives for image segmentation. Such approaches consider the pixels as data in a multidimensional space and using different rules thy classify the information into different groups according to the features. This chapter presents the clustering algorithms that are commonly used for image segmentation.
An Implementation and Evaluation of Social Media System to Support Co-Creation Activities in Multiple Regions

Regional revitalization is required in Japan because the issues caused by the declining birthrate, aging population, and depopulation etc., are increasing in rural area. New problem-solving methods which are generated through co-creation are required in regional revitalization. In co-creation, social media is important in order to continuation and sharing of activities. In the past, social media existed to support co-creation in order to solve issues in specific or global area, but there were no social media that support to solve concrete issues common to multiple regions. This study proposes social media to support co-creative activities in order to solve regional issues common to multiple regions. Promotion and sharing of co-creative activities are important factors in this social media. The social media we named "MiraiLab" was designed to satisfy these two factors. To confirm the effectiveness of “MiraiLab,” social experiments were conducted in “Special Interest Group on Crowd Co-creation Intelligence (SIG-CCI)” at The Japanese Society for Artificial Intelligence. As results, it was found that “MiraiLab” was effective for the promotion and sharing of co-creative activities. Specifically, regarding promotion, “MiraiLab” contributed to the continuation of co-creative activities and the creation of outcomes. Regarding sharing, it was found that co-creative activities were shared between projects, between research groups, and between account registrants and non-registrants through “MiraiLab”.
Tsallis Entropy for Image Thresholding

One interesting functional criterion for image thresholding is the Tsallis entropy, which gives excellent results in bi-level segmentation. However, when it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the number of evaluations of the objective function. Under such circumstances, most of the optimization algorithms do not seem to be suited to face such problems as they usually require many evaluations before delivering an acceptable result. This chapter introduces the use of evolutionary algorithms to improve segmentation process using the Tsallis entropy for search the best thresholds.
From quantum coherence to nonclassicality and metrological power

We discuss the relationship between quantum coherence of finite dimensional systems and nonclassical quantum light. We demonstrate that quantifying the quantum coherence between coherent states always leads to a quantifier of nonclassicality that monotonically decreases under linear optical operations. This allows us to introduce a resource theory of nonclassicality based on linear optics that closely parallels the resource theory of coherence. Finally, we discuss the metrological power of a quantum state, which quantifies how sensitive a quantum state is to displacement operations. It is then shown that the metrological power leads to a nonclassicality monotone.