Reviewerly/exHarmony huggingface dataset:
Application of derived numbers theory in convex analysis

The Fermat, Roll and Lagrange theorems are generalized into the class of nondifferentiable functions, the necessary and sufficient conditions for monotonicity and convexity of one variable functions are given.
A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts

Predicting next point of interest (POI) of users in location-based social networks has become an increasingly significant requirement, because of its potential benefits for individuals and businesses. Recently, various recurrent neural network architectures have incorporated contextual information associated with users’ sequence of check-ins to capture their dynamic preferences. However, these architectures are limited because they only take the sequential order of check-ins into account and face difficulties in remembering long-range dependencies. In this work, we resort to the heterogeneous of information network (HIN) to address these issues. Specifically, a novel attentional meta-path-based recurrent neural network is proposed, dubbed ST-HIN. ST-HIN predicts the next POI of users from their spatial–temporal incomplete historical check-in sequences, and uses the multi-modal recurrent neural network to capture the complex transition relationship. Furthermore, a meta-path attention embedding module is devised to capture the mutual influence between the users’ meta-path-based global information in HIN and the dynamic status of their current mobility. The results of extensive experiments performed on real-world datasets demonstrate the effectiveness of our proposed model.
Novel Quality Metric for Image Super Resolution Algorithms - Super Resolution Entropy Metric (SREM)

Even with the topical developments of numerous image Super Resolution (SR) algorithms, how to quantify the visual quality scores of a super resolved image is still an open research problem. Majority of SR images are evaluated by full-reference metric with the support of a reference image. There are some circumstances when a reference image is unavailable or is with degraded quality. We propose a super resolution benchmark Super Resolution Entropy Metric (SREM) which can be used to evaluate the effectiveness of pixel reconstruction and quality of the image in the absence of reference image automatically. SREM measures the experimental quality of an SR image based on the perceptions of acutance and spatial discontinuity features in the gradient domain and wavelet domain. Experimental scores illustrate that the SREM metric is competent for assessing the visual quality of super-resolved images.
Can Distributed Uniformity Testing Be Local?

In the distributed uniformity testing problem, k servers draw samples from some unknown distribution, and the goal is to determine whether the unknown distribution is uniform or whether it is ε-far from uniform, where ε is a proximity parameter. Each server decides whether to accept or reject, and these decisions are sent to a referee, who makes a final decision based on the servers' local decisions. Uniformity testing is a particularly useful building-block, because it is complete for the problem of testing identity to any fixed distribution.
The cryptanalysis of the Rabin public key algorithm using the Fermat factorization method

Abstract As a public key cryptography algorithm, the Rabin algorithm has two keys, i.e. , public key (n) and private key (p, q). The security of Rabin algorithm relies on the difficulty of factoring very large numbers, so the greater the private keys are used, the better the security becomes. In order to test how hard it is to cryptanalyze the Rabin public key n, we use the Fermat factorization method to obtain the values of p and q. After obtaining the factors, both of these factors are tested whether or not they are in accordance with the Rabin private key requirements. The first is to test whether or not the factors are prime numbers using Fermat Little Theorem. The second is to test whether or not the factors are congruent to 3 in modulo 4. If the results of the two tests turn out to be positive, then the factors are indeed p and q, the private keys of the Rabin algorithm. The result of our experiment indicates that the value of public key n does not have a directly proportional correlation to the factoring time. A factor which may affect the factoring time is the difference between the private keys (p - q): the larger the difference, the longer the factoring time.
Nonuniform Families of Polynomial-Size Quantum Finite Automata and Quantum Logarithmic-Space Computation with Polynomial-Size Advice

The state complexity of a finite(-state) automaton intuitively measures the size of the description of the automaton. Sakoda and Sipser [STOC 1972, pp. 275–286] were concerned with nonuniform families of finite automata and they discussed the behaviors of nonuniform complexity classes defined by families of such finite automata having polynomial-size state complexity. In a similar fashion, we introduce nonuniform state complexity classes using families of quantum finite automata. Our primarily concern is one-way quantum finite automata empowered by garbage tapes. We show inclusion and separation relationships among nonuniform state complexity classes of various one-way finite automata, including deterministic, nondeterministic, probabilistic, and quantum finite automata of polynomial size. For two-way quantum finite automata equipped with garbage tapes, we discover a close relationship between the nonuniform state complexity of such a polynomial-size quantum finite automata family and the parameterized complexity class induced by quantum logarithmic-space computation assisted by polynomial-size advice.
Using Simulation for Locating Transmitter in a Multistatic Sensor Network

Multistatic sensor networks consist of independent transmitters (sources)and receivers with a shared area coverage. Some important applications of bi/multistatic sensors in space systems include the use of spatially separated receiver and transmitter satellites, design of space radars and spaceborn bi/multistatic synthetic aperture radar. Sensing zone of a single transmitter-receiver couple in such systems is defined by Cassini ovals, a family of quartic plane curves with unique properties. Therefore, the problem of locating sensors in multistatic settings is a challenge for decision-makers. In this study, considering a given set of receiver locations, we aim to determine the location for a single transmitter to maximize the total area covered. To achieve this we carry out exhaustive Monte Carlo simulations which measure the coverage achieved for all possible transmitter location alternatives for a given receiver field. We experiment with both randomly and polygonally deployed receiver locations and present results for a number of selected examples. Our work reveals that the coverage can significantly be improved by selecting appropriate transmitter locations and simulation is a useful tool to design multistatic fields.
Exploring Expansion and Innovations in Cloud Computing

Cloud computing is one of the highly sought-after paradigms in information technology. In several cases, it has surpassed earlier predictions of growth, and expanding its services to cover all the key areas. With the growing usage of the cloud, new requirements have also surfaced. To meet user expectations, the cloud services pool has expanded drastically. In order to meet the subscriber's futuristic demands, cloud computing needs to advance further. This work undertakes the study of expansion and innovations that have already happened in the recent past. In addition, perceived cloud evolution in the futuristic cloud has been presented. During the course of exploration, the impact of hardware and software on evolution has been taken into account. Considering the benefits involved, and the current advancement, this work concludes by presenting the innovations that will lead to cloud development.
New RTI Technology for Palæography

A new illumination dome has been designed for palaeographic use.It has the innovation that the majority of the 128 LED lights are concentrated at low angles of elevation, to increase angular resolution and enable finer discrimination of surface features.Image processing techniques applied to subsets of the images enable the enhancement of surface features.A new web-based OxRTIViewer provides options for multiple annotation and drawing layers, enabling palaeographers anywhere easily to view each other's interpretations and collaborate across the internet.With this new system, we are creating a platform for crowd-sourcing of epigraphy, opening a new path for collaborative digital scholarship and direct public engagement with Roman writing tablets.
FACS at 40

Creating emotionally impactful characters has been one of the biggest challenges in computer graphics. Key to this has been understanding how faces express emotion. In 1978 research started in coding human expressions. The Facial Action Coding System (FACS) would become one of the cornerstones of facial animation of digital characters. Today FACS is recognized as the gold standard in scientific facial research and animation. Forty years on, we discuss the strengths and limitations of FACS, its relevance in the age of Machine Learning and what people are doing to improve upon FACS in animation.
The Performance of One Dimensional Naïve Bayes Classifier for Feature Selection in Predicting Prospective Car Insurance Buyers

One of the products sold by insurance companies is car insurance. To offer this product, one of the techniques used by the company is cold calling. This method often decreases the sellers’ mentalities because they face many rejections when offering insurance products. This problem can be reduced by classifying prospective buyers’ data first. The data can be classified as customers with the potential to buy insurance and customers who have no potential to buy insurance. From the obtained data, there are certainly many features that support the classification process. However, not all features contributed to improving classification accuracy. Machine learning especially the method of feature selection helps to reduce dimensions and to improve classification accuracy. In this paper, we examine One-Dimensional Naïve Bayes Classifier (1-DBC) as a feature selection method that is applied to two classifier methods, i.e., Support Vector Machine and Logistic Regression. Our simulations show that the two classifiers can use fewer features to produce comparable accuracies in classifying prospective car insurance buyers.
String periods in the order-preserving model

In the order-preserving model, two strings match if they share the same relative order between the characters at the corresponding positions. This model is quite recent, but it has already attracted significant attention because of its applications in data analysis. We introduce several types of periods in this setting (op-periods). Then we give algorithms to compute these periods in time O(n), O(nlog⁡log⁡n), O(nlog2⁡log⁡n/log⁡log⁡log⁡n), O(nlog⁡n) depending on the type of periodicity. In the most general variant, the number of different op-periods can be as big as Ω(n2), and a compact representation is needed. Our algorithms require novel combinatorial insight into the properties of op-periods. In particular, we characterize the Fine–Wilf property for coprime op-periods.
Evaluation of NUMA-Aware Scheduling in Warehouse-Scale Clusters

Non-uniform memory access (NUMA) has been extensively studied at the machine level but few studies have examined NUMA optimizations at the cluster level. This paper introduces a holistic NUMA-aware scheduling policy that combines both machine-level and cluster-level NUMA-aware optimizations. We evaluate our holistic NUMA-aware scheduling policy on Google's production cluster trace with a cluster scheduling simulator that measures the impact of NUMAaware scheduling under two scheduling algorithms, Best Fit and Enhanced PVM (E-PVM). While our results highlight that a holistic NUMA-aware scheduling policy substantially increases the proportion of NUMA-fit tasks by 22.0% and 25.6% for both the Best Fit and E-PVM scheduling algorithms, respectively, there is a non-trivial tradeoff between cluster job packing efficiency and NUMA-fitness for the E-PVM algorithm under certain circumstances.
Precious metal mineralogy of the Murtykty gold deposit, South Urals

Ore mineralogy of the Murtykty gold deposit is presented in the paper and main attention is paid to the mode of occurrence of precious metals. Ores are pyrite-bearing quartz-chlorite (±sericite, ±carbonate of the dolomite-ankerite series) metasomatites with variable ratios between rock-forming minerals. Pyrite is the major sulfde; sphalerite, galena and chalcopyrite are secondary in abundance. Rare minerals include pyrrhotite, arsenopyrite, altaite, coloradoite, hessite, petzite, calaverite, volynskite, rucklidgeite, and native gold. The Ag content of native gold ranges from 6.11 to 35.32 wt. %. Signifcant amount of Au and Ag occurs in a telluride form: hessite Ag2Te, petzite Ag3AuTe2, calaverite AuTe2, and volynskite AgBiTe2. The refractory features of sulfde ores are caused by diverse modes of occurrences of precious metal.
Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

SUMMARY In order to solve joint-angle drift problem of dual redundant manipulators at acceleration-level, an acceleration-level tri-criteria optimization motion planning (ALTC-OMP) scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor. This scheme can resolve the joint-angle drift problem of dual redundant manipulators which will arise in single criteria or bi-criteria scheme. In addition, the proposed scheme considers joint-velocity joint-acceleration physical limits. The proposed scheme can not only guarantee joint-velocity and joint-acceleration within their physical limits, but also ensure that final joint-velocity and joint-acceleration are near to zero. This scheme is realized by dual redundant manipulators which consist of left and right manipulators. In order to ensure the coordinated operation of manipulators, two motion planning problems are reformulated as two general quadratic program (QP) problems and further unified into one standard QP problem, which is solved by a simplified linear-variational-inequalities-based primal-dual neural network at the acceleration-level. Computer-simulation results based on dual PUMA560 redundant manipulators further demonstrate the effectiveness and feasibility of the proposed ALTC-OMP scheme to resolve joint-angle drift problem arising in the dual redundant manipulators.
Evolvement Constrained Adversarial Learning for Video Style Transfer

Video style transfer is a useful component for applications such as augmented reality, non-photorealistic rendering, and interactive games. Many existing methods use optical flow to preserve the temporal smoothness of the synthesized video. However, the estimation of optical flow is sensitive to occlusions and rapid motions. Thus, in this work, we introduce a novel evolve-sync loss computed by evolvements to replace optical flow. Using this evolve-sync loss, we build an adversarial learning framework, termed as Video Style Transfer Generative Adversarial Network (VST-GAN), which improves upon the MGAN method for image style transfer for more efficient video style transfer. We perform extensive experimental evaluations of our method and show quantitative and qualitative improvements over the state-of-the-art methods.
On matching numbers of tree and bipartite degree sequences

We study the possible values of the matching number among all trees with a given degree sequence as well as all bipartite graphs with a given bipartite degree sequence. For tree degree sequences, we obtain closed formulas for the possible values. For bipartite degree sequences, we show the existence of realizations with a restricted structure, which allows to derive an analogue of the Gale–Ryser Theorem characterizing bipartite degree sequences. More precisely, we show that a bipartite degree sequence has a realization with a certain matching number if and only if a cubic number of inequalities similar to those in the Gale–Ryser Theorem are satisfied. For tree degree sequences as well as for bipartite degree sequences, the possible values of the matching number form intervals.
Extracting actionable knowledge from social networks with node attributes

Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable knowledge in each specific domain. An action is a new tool in this research area that suggests some changes to the user to gain a profit in his/her domain. Currently, most of action mining methods rely on simple data which describes each object independently. Since social data has more complex structure due to the relationships between individuals, a major problem is that such structural information is not taken into account in the action mining process. This leads to miss some useful knowledge and profitable actions. Consequently, more effective methods are needed for mining actions. The main focus of this work is to extract cost-effective actions from social networks in which nodes have attributes. The actions suggest optimal changes in nodes’ attributes that are likely to result in changing labels of users to more desired one when they are applied. We develop an action mining method based on Random Walks that naturally combines the information from the network structure with nodes attributes. We formulate action mining as an optimization problem where the goal is to learn a function that varies the values of nodes’ attributes which in turn affect edges’ weights in the network so that the labels of intended individuals are likely to take the desired label while minimizing the cost of incurring the changes. Experiments confirm that the proposed approach outperforms the current state-of-the-art in action mining.
A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

Nature-inspired algorithms are based on the concepts of self-organization and complex biological systems. They have been designed by researchers and scientists to solve complex problems in various environmental situations by observing how naturally occurring phenomena behave. The introduction of nature-inspired algorithms has led to new branches of study such as neural networks, swarm intelligence, evolutionary computation, and artificial immune systems. Particle swarm optimization (PSO), social spider optimization (SSO), and other nature-inspired algorithms have found some success in solving clustering problems but they may converge to local optima due to the lack of balance between exploration and exploitation. In this paper, we propose a novel implementation of SSO, namely social spider optimization for data clustering using single centroid representation and enhanced mating operation (SSODCSC) in order to improve the balance between exploration and exploitation. In SSODCSC, we implemented each spider as a collection of a centroid and the data instances close to it. We allowed non-dominant male spiders to mate with female spiders by converting them into dominant males. We found that SSODCSC produces better values for the sum of intra-cluster distances, the average CPU time per iteration (in seconds), accuracy, the F -measure, and the average silhouette coefficient as compared with the K -means and other nature-inspired techniques. When the proposed algorithm is compared with other nature-inspired algorithms with respect to Patent corpus datasets, the overall percentage increase in the accuracy is approximately 13%. When it is compared with other nature-inspired algorithms with respect to UCI datasets, the overall percentage increase in the F -measure value is approximately 10%. For completeness, the best K cluster centroids (the best K spiders) returned by SSODCSC were specified. To show the significance of the proposed algorithm, we conducted a one-way ANOVA test on the accuracy values and the F -measure values returned by the clustering algorithms.
A Unified Framework of Deep Neural Networks by Capsules

Deep learning has made a great deal of success in processing images, audios, and natural languages. With the growth of deep learning, how to describe deep neural networks unifiedly is becoming an important issue. We first formalize neural networks mathematically with their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs. Then, we set up a unified framework for deep learning with capsule networks. This capsule framework could simplify the description of existing deep neural networks, and provide a theoretical basis of graphic designing and programming techniques for deep learning models, thus would be of great significance to the advancement of deep learning.
Bayesian nonparametric clustering as a community detection problem

A wide class of Bayesian nonparametric priors leads to the representation of the distribution of the observable variables as a mixture density with an infinite number of components. Such a representation induces a clustering structure in the data. However, due to label switching, cluster identification is not straightforward a posteriori and some post-processing of the MCMC output is usually required. Alternatively, observations can be mapped on a weighted undirected graph, where each node represents a sample item and edge weights are given by the posterior pairwise similarities. It is shown how, after building a particular random walk on such a graph, it is possible to apply a community detection algorithm, known as map equation, leading to the minimisation of the expected description length of the partition. A relevant feature of this method is that it allows for the quantification of the posterior uncertainty of the classification.
Uncertainty Constrained Robotic Exploration: An Integrated Exploration Planner

Efficient robotic exploration of unknown sensor limited global-information-deficient environments poses unique challenges to path planning algorithms. In these difficult environments, no deterministic guarantees on path completion and mission success can be made in general. Integrated exploration (IE), which strives to combine localization and exploration, must be solved in order to create an autonomous robotic system capable of long-term operation in new and challenging environments. This paper formulates a probabilistic framework that allows the creation of exploration algorithms providing probabilistic guarantees of success. A novel connection is made between the Hamiltonian path problem and exploration. The guaranteed probabilistic information explorer (G-PIE) is developed for the IE problem, providing a probabilistic guarantee on path completion, and asymptotic optimality of exploration. A receding horizon probabilistic information explorer (RH-PIE) is presented, which addresses the exponential complexity present in G-PIE. Finally, RH-PIE planner is verified via autonomous hardware-in-the-loop experiments.