Reviewerly/exHarmony huggingface dataset:
Estilos de aprendizaje: estrategias de enseñanza en LUZ

The development of the future professional graphic design requires the constant use of didactic and financial resources for learning, the product of a growing market and with greater demands due to global growth. This directs the way to the use of teaching strategies in correspondence with the style of student learning that strengthen their potentialities. The objective is to evaluate the learning styles present in the students assigned to the Graphic Design program of FADLUZ, to generate teaching and motivational strategies that stimulate their learning. A descriptive and field research was considered, the sample consisted of 59 Students active and enrolled in the curricular units of the Instrumental Axis of the Graphic Design Program of the FADLUZ who were administered the Kolb Learning Styles Inventory of 1985 through a form in Google Form. Most students learn using Reflective Observation (OR) and are inclined primarily to two learning styles. The Assimilator with 51% followed by Divergent with 32% identified that the trend is towards a more balanced learning. To conclude that the identification of the type of learning style in Graphic Design students does not lead to a particular type of active teaching strategy, even though efforts can be focused on the design of activities, selection of educational material, presenting information, creating or not working groups and modifying the aspects to be evaluated. It is important for the student to identify their learning style to strengthen their training process. Likewise, motivation should be fostered that fosters the interest to learn, without neglecting the fact of knowing what strategies can be implemented according to the style of learning.
Review Paper on Face Recognition Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Security and Privacy Issues in Fog driven IoT Environment

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Review and future directions of Fault Tolerance schemes and applied techniques in Wireless Sensor Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Survey on Load Balancing Algorithms in Cloud Computing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Emotion Identification between POMS and Multinomial Naive Bayes Algorithm Using Twitter API

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Investigation on Stationarity of V2V Channels in a Highway Scenario

This contribution investigates the stationarity of the vehicle to vehicle (V2V) channel in terms of distance and time. Due to high inherent mobility, the channel can not be assumed to follow the wide sense stationary (WSS) and uncorrelated scattering (US) assumption. Therefore, new evaluation methods have to be applied. We assess the stationarity for a V2V highway scenario using the generalized local scattering function (GLSF) and its collinearity based on measurements. We compare results for exemplary traffic situations and investigate the influence of the antenna placements on the stationarity of the channel. Our results show a strong relation between the stationarity time and the change and rate-of-change of distance between transmitter and receiver.
Apache Hadoop A Guide for Cluster Configuration and Testing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Color Image Encryption using Single Layer Artificial Neural Network and Buffer Shuffling

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Worldreader: Leveraging Mobile Phones for Reading to Young Children in India and Jordan

Access to education can be increased, even for previously marginalized populations, by harnessing technologies, such as mobile phones, that have become affordable options for communication and connectivity around the world.
Exploiting Glue Clauses to Design Effective CDCL Branching Heuristics

In conflict-directed clause learning (CDCL) SAT solving, a state-of-the-art criterion to measure the importance of a learned clause is called literal block distance (LBD), which is the number of distinct decision levels in the clause. The lower the LBD score of a learned clause, the better is its quality. The learned clauses with LBD score of 2, called glue clauses, are known to possess high pruning power. In this work, we relate glue clauses to decision variables. First, we show experimentally that branching decisions with variables appearing in glue clauses, called glue variables, are more conflict efficient than with nonglue variables. This observation motivated the development of a structure-aware CDCL variable bumping scheme, which increases the heuristic score of a glue variable based on its appearance count in the glue clauses that are learned so far by the search. Empirical evaluation shows the effectiveness of the new method on the main track instances from SAT Competitions 2017 and 2018 with four state-of-the-art CDCL SAT solvers. Finally, we show that the frequency of learned clauses that are glue clauses can be used as a reliable indicator of solving efficiency for some instances, for which the standard performance metrics fail to provide a consistent explanation.
K-TanH: Hardware Efficient Activations For Deep Learning.

We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning. K-TanH consists of parameterized low-precision integer operations, such as, shift and add/subtract (no floating point operation needed) where parameters are stored in very small look-up tables that can fit in CPU registers. K-TanH can work on various numerical formats, such as, Float32 and BFloat16. High quality approximations to other activation functions, e.g., Sigmoid, Swish and GELU, can be derived from K-TanH. Our AVX512 implementation of K-TanH demonstrates $>5\times$ speed up over Intel SVML, and it is consistently superior in efficiency over other approximations that use floating point arithmetic. Finally, we achieve state-of-the-art Bleu score and convergence results for training language translation model GNMT on WMT16 data sets with approximate TanH obtained via K-TanH on BFloat16 inputs.
Research on Online Detection Method of Power Quality Monitoring Device and System Design

As power quality monitoring equipment is running in harsh electromagnetic environment, the measurement accuracy is difficult to be guaranteed. Because of the lack of on-site test technology, it is difficult to test the accuracy of the power quality monitoring equipment which is already installed in substation. In order to solve the problem, an on-site testing system of power quality monitoring equipment is designed in this paper. By adopting high precision portable power quality monitoring equipment as standard ratio equipment, high precision IEEE1588 time synchronization technology, automatic reading standard ratio equipment and testing equipment monitoring data, and automatically calculating error, automatic on-site test of power quality monitoring equipment can be achieved. The laboratory test shows that the on-site testing system for power quality monitoring equipment is reliable, and it is suitable for testing the measurement accuracy of the power quality monitoring equipment which is operated in the substation.
Asymmetric Einstein–Podolsky–Rosen steering manipulating among multipartite entangled states

Asymmetric Einstein–Podolsky–Rosen (EPR) steering means the ability of one party to nonlocally “steer” the state in the other side through local measurements. The asymmetric property of EPR steering provides adequate security in one-sided device-independent secure quantum communication protocols. In this paper, we theoretically investigate the asymmetric EPR steering schemes between two subsystems of multipartite entangled states by an asymmetric modulation method. Two schemes, including tripartite entangled state to bipartite steering and quadripartite entangled state to tripartite steering, are investigated. By reconstructing the covariance matrix of continuous variable multipartite Gaussian states, we quantify the steerability of manipulating two different subsystems and demonstrate the EPR steering parameter versus various types of states’ parameters. Our work provides a reference for asymmetric quantum information processing and enriches resources for secure quantum networks.
Correction to: A robust image steganography based on the concatenated error correction encoder and discrete cosine transform coefficients

The article “A robust image steganography based on the concatenated error correction encoder and discrete cosine transform coefficients” written by Zhenkun Bao et al. was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 11 June 2019 with open access.
Correction of start-up time difference-induced measurement errors of a high-speed binocular stereovision system

In a high-speed stereovision system, high-speed cameras (HSCs) are used to record images and time information, and the measurement accuracy depends on the time precision of the cameras. When two HSCs are used in a binocular stereovision system, the image acquisitions of both cameras are unlikely to start simultaneously. The images that are supposed to be captured at the “same time” may record different states of the test object during an experiment, which will cause measurement errors. In this work, a theoretical model was established to correlate the start-up time difference (STD) and the STD-induced measurement errors of a binocular stereovision system. A simulation of the deflection of a cantilever beam verified the accuracy of the theoretical model. A suggestion of the experimental layout was discussed to reduce the STD-induced measurement errors in one direction, and a method was proposed to correct the STD-induced measurement errors. The effectiveness of the correction method was validated by two experiments, that is, the trajectory of a marker point and the out-of-plane deformation of a bubble. The results showed that the absolute errors of the marker point trajectory were almost negligible and the deformation contours of the bubble were more symmetric after the correction. This correction method should be useful for three-dimensional measurements of physical quantities using a binocular stereovision system.
Domain Information Enhanced Dependency Parser

Dependency parsing has been an important task in the natural language processing (NLP) community. Supervised methods have achieved great success these years. However, these models can suffer significant performance loss when test domain differs from the training domain. In this paper, we adopt the Bi-Affine parser as our baseline. To explore domain-specific information and domain-independent information for cross-domain dependency parsing, we apply an ensemble-style self-training and adversarial learning, respectively. We finally combine the two strategies to enhance our baseline model and our final system was ranked the first of at NLPCC2019 shared task on cross-domain dependency parsing.
Depth-map estimation using combination of global deep network and local deep random forest

This study propose a robust 3D depth-map generation algorithm using a single image. Unlike previous related works estimating global depth-map using deep neural networks, this study uses the global and local feature of image together to reflect local changes in the depth map instead of using only global feature. A coarse-scale network is designed to predict the global-coarse depth map structure using a global view of the scene and the finer-scale random forest (RF) is to be designed to refine the depth map based on combination of original image and coarse depth map. As the first step, we use a partial structure of the multi-scale deep network (MSDN) to predict the depth of the scene at a global level. As the second step, we propose local patchbased deep RF to estimate the local depth and smoothen noise of local depth map by combining MSDN global-coarse network. The proposed algorithm was successfully applied to various single images and yielded a more accurate depthmap estimation performance than other existing methods.
Immersive and Interactive Visualisation of a Virtual Honey Bee Colony

Social insects and more specifically honey bees have very complex, powerful and interesting task allocation abilities. They are able to distribute their workforce effectively without any central control, using simple mechanisms based on stimuli (dances, pheromones), interactions, thresholds and feedback loops. Self-organisational concepts and some of those mechanisms, like pheromones, are invisible to the human eye. In order to help the user grasp the complexity of the task allocation, we propose to make them visible in a virtual, immersive and interactive environment. First, this implies that the system must be able to simulate and display in real-time around 30 000 bees interacting with each other, emitting clouds of pheromones. Secondly, this implies also that the user should be able to alter in real-time the environment of the bees (e.g. by manipulating the frame of the hive) and visualise the effects on the organisation, potentially days later. Finally, we would like to give to biologists and beekeepers some domain-related, intuitive and natural ways of interacting with the hive. We describe in this article these issues in more details, and how we plan to tackle them. This is a “work in progress”, therefore a lot of work has still to be done, mostly surveying and modelling the interactions.
Sorcar: Property-Driven Algorithms for Learning Conjunctive Invariants

We present a new learning algorithm Sorcar to synthesize conjunctive inductive invariants for proving that a program satisfies its assertions. The salient property of this algorithm is that it is property-driven, and for a fixed finite set of n predicates, guarantees convergence in 2n rounds, taking only polynomial time in each round. We implement and evaluate the algorithm and show that its performance is favorable to the existing Houdini algorithm (which is not property-driven) for a class of benchmarks that prove data race freedom of GPU programs and another class that synthesizes invariants for proving separation logic properties for heap manipulating programs.
Translator2Vec: Understanding and Representing Human Post-Editors

The combination of machines and humans for translation is effective, with many studies showing productivity gains when humans post-edit machine-translated output instead of translating from scratch. To take full advantage of this combination, we need a fine-grained understanding of how human translators work, and which post-editing styles are more effective than others. In this paper, we release and analyze a new dataset with document-level post-editing action sequences, including edit operations from keystrokes, mouse actions, and waiting times. Our dataset comprises 66,268 full document sessions post-edited by 332 humans, the largest of the kind released to date. We show that action sequences are informative enough to identify post-editors accurately, compared to baselines that only look at the initial and final text. We build on this to learn and visualize continuous representations of post-editors, and we show that these representations improve the downstream task of predicting post-editing time.
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients

In recent years, advances in deep learning have enabled the application of reinforcement learning algorithms in complex domains. However, they lack the theoretical guarantees which are present in the tabular setting and suffer from many stability and reproducibility problems \citep{henderson2018deep}. In this work, we suggest a simple approach for improving stability and providing probabilistic performance guarantees in off-policy actor-critic deep reinforcement learning regimes. Experiments on continuous action spaces, in the MuJoCo control suite, show that our proposed method reduces the variance of the process and improves the overall performance.
Hybrid Encryption from McEliece Cryptosystem with Pseudo-random Error Vector

We propose a new hybrid encryption scheme to use with McEliece cryptosystem. The hybrid scheme uses specific authenticated encryption scheme for the encryption of the plaintext. The symmetric key is embedded in reversible way into the error vector of the McEliece cryptosystem. CCA2 security is prov ided by the symmetric part of the scheme. The embedding is done in such a way, that the error vector cannot be distinguished from a randomly chosen one. An eXtensible Output Function can be used to enable variable length conversion from (pseudo-random) bit strings to error vectors. The encryption part can be implemented in a streamed way, so the sender does not have to store the whole message in the memory.