Reviewerly/exHarmony huggingface dataset:
Layer-by-layer Quantization Method for Neural Network Parameters

Limited by the storage and computing power of mobile devices, the deployment of neural networks on mobile devices is slow. Quantifying the parameters in the neural network not only reduces the storage required by the network, but also simplifies the design of the arithmetic unit. This facilitates the application of neural networks in mobile devices. This paper proposes a novel parameter quantization method, which quantifies the weight data and output data of the network layer by layer to achieve the purpose of model compression and achieve a good balance between model size and precision. The method in this paper achieves a 7.62 times model compression on the MNIST dataset with a precision loss of only 0.13%.
Observation of silica nanoparticle growth in saline geothermal brine from the Yamagawa geothermal power station, Japan, using dynamic light scattering

Silica nanoparticle growth during the polymerization of silicic acid in saline geothermal brine from the Yamagawa geothermal power station, Japan, was examined using dynamic light scattering (DLS). Two types of polysilicic acid particles (large and small) were found to grow simultaneously after vapor–brine separation at the surface. The DLS results seemed to indicate that the average particle size increased over the initial 70 min and then decreased, however this is most likely due to the larger particles settling out. The small particles gradually increased to 200 nm over 180 min. As deduced from the change in the numbers of the two types of polysilicic acid particles, the small particles were absorbed by the large particles.
Food Image to Cooking Instructions Conversion Through Compressed Embeddings Using Deep Learning

The image understanding in the era of deep learning is burgeoning not only in terms of semantics but also in towards the generation of a meaningful descriptions of images, this requires specific cross model training of deep neural networks which must be complex enough to encode the fine contextual information related to the image and simple enough enough to cover wide range of inputs. Conversion of food image to its cooking description/instructions is a suitable instance of the above mentioned image understanding challenge. This paper proposes a unique method of obtaining the compressed embeddings of cooking instructions of a recipe image using cross model training of CNN, LSTM and Bi-Directional LSTM. The major challenge in this is variable length of instructions, number of instructions per recipe and multiple food items present in a food image. Our model successfully meets these challenges through transfer learning and multi-level error propagations across different neural networks by achieving condensed embeddings of cooking instruction which have high similarity with original instructions. In this paper we have specifically experimented on Indian cuisine data (Food image, Ingredients, Cooking Instruction and contextual information) scraped from the web. The proposed model can be significantly useful for information retrieval system and it can also be effectively utilized in automatic recipe recommendations.
<i>Mi(s) Lengua(s)</i>: educational paths for Hispanic students within Italian school

The Italian linguistic space has radically changed through the onset of the new millennium due to the presence of innovative multiple linguistic practices that have taken place as a consequence of deterritorialization processes. Furthermore, Latino diaspora in recent years have fostered the appearance of new forms of Global Spanishes (García and Otheguy 2014. "Spanish and Hispanic Bilingualism." In The Routledge Handbook of Hispanic Applied Linguistics, edited by Manuel Lacorte, 639–658. London: Routledge) in many contexts of Northern Italy where Hispanic communities are numerous. In spite of the global sociolinguistic changes, the forms of minority bilingual practices are submitted to ideological stigmatisation by many public institutions. This is the case of educational settings, where the presence of not-vernacular repertoires can be considered an obstacle for transnational students although the expansion of all the codes that make up the bilingual repertoire is a key element towards students' proper sociocognitive development. With this background in mind, I will offer the results of the action research Mi(s) lengua(s), an educational project inspired by a glottopolitical perspective, aimed at the inclusion of home language and culture in the educational paths of a group of middle school Hispanic students in Milan.
An enriched mixture model for functional clustering

Abstract There is an increasingly rich literature about Bayesian nonparametric models for clustering functional observations. Most recent proposals rely on infinite‐dimensional characterizations that might lead to overly complex cluster solutions. In addition, while prior knowledge about the functional shapes is typically available, its practical exploitation might be a difficult modeling task. Motivated by an application in e‐commerce, we propose a novel enriched Dirichlet mixture model for functional data. Our proposal accommodates the incorporation of functional constraints while bounding the model complexity. We characterize the prior process through a urn scheme to clarify the underlying partition mechanism. These features lead to a very interpretable clustering method compared to available techniques. Moreover, we employ a variational Bayes approximation for tractable posterior inference to overcome computational bottlenecks.
Data-Driven Enhanced Nonlinear Gaussian Filter

Nonlinear Gaussian filters are usually developed from numerical quadrature rules to approximate the mean and covariance. However, the Gaussian assumption may not be viable after propagation of the estimate through nonlinear dynamics, which may lead to degraded performance. In this brief, we propose an enhanced nonlinear Gaussian quadrature filter in which the quadrature points/weights are generated by a data-driven arbitrary polynomial chaos (aPC) method without relying on the Gaussian assumption. A set of Monte Carlo samples are first propagated through nonlinear dynamics. The statistic moments can be calculated directly from these Monte Carlo samples. The enhanced quadrature points/weights are generated from these moments using the aPC method. Since such quadrature points contain higher order statistic information of the propagated distribution, they can better represent the state distribution and provide a more accurate estimate. Numerical examples show the effectiveness of the proposed filter.
Checking certificate revocation efficiently using certificate revocation guard

In the Public Key Infrastructure (PKI) model, digital certificates play a vital role in securing online communication. Communicating parties exchange and validate these certificates and the validation should fail if the certificate has been revoked. However, some existing studies [1,2] raise an alarm as the certificate revocation check is skipped in the existing PKI model for a number of reasons including network latency overheads, bandwidth costs, storage costs and privacy issues. In this article, we propose a Certificate Revocation Guard (CRG) to efficiently check certificate revocation while minimising bandwidth, latency and storage overheads. CRG is based on OCSP, which caches the revocation status of certificates locally, thus strengthening user privacy for subsequent requests. CRG is a plug and play component that could be installed on the user's machine, at the organisational proxy or even in the ISP network. Compared to a naive approach (where a client checks the revocation status of all certificates in the chain on every request), CRG decreases the bandwidth overheads and network latencies by 95%. Using CRG incurs 69% lower storage overheads compared to the CRL method. Our results demonstrate the effectiveness of our approach to improve the certificate revocation process.
Search Result Reranking with Visual and Structure Information Sources

Relevance estimation is among the most important tasks in the ranking of search results. Current methodologies mainly concentrate on text matching, link analysis, and user behavior models. However, users judge the relevance of search results directly from Search Engine Result Pages (SERPs), which provide valuable signals for reranking. In this article, we propose two different approaches to aggregate the visual, structure, as well as textual information sources of search results in relevance estimation. The first one is a late-fusion framework named Joint Relevance Estimation model (JRE). JRE estimates the relevance independently from screenshots, textual contents, and HTML source codes of search results and jointly makes the final decision through an inter-modality attention mechanism. The second one is an early-fusion framework named Tree-based Deep Neural Network (TreeNN), which embeds the texts and images into the HTML parse tree through a recursive process. To evaluate the performance of the proposed models, we construct a large-scale practical Search Result Relevance (SRR) dataset that consists of multiple information sources and relevance labels of over 60,000 search results. Experimental results show that the proposed two models achieve better performance than state-of-the-art ranking solutions as well as the original rankings of commercial search engines.
Eczema, Hives and Psoriasis Detection with the Application of Local Binary Pattern, Color Histogram, SVM and RGB-HSV Color Space

This paper uses a system that can recognize and detect skin diseases such as hives, eczema and psoriasis and provide temporary relief using RGH-HSV Color Space, Local Binary Pattern, Color Histogram and Support Vector Machine (SVM). These algorithms were used to train images for the testing process of the system. For the controlled testing the researchers gathered 30 images for each disease, eczema, hives and psoriasis, from dermnet.com and images from people that have been already diagnosed with the mentioned diseases in both training the prototype and testing the system's accuracy. The system identifying eczema, hives, and psoriasis have the following accuracies respectively, 96.0560%, 95.8041%, and 98.5609%.
Do Extracted State Machine Models Help to Understand Embedded Software?

Program understanding is a prerequisite for several software activities, such as maintenance, evolution, and reengineering. Code in itself is so detailed that it is often hard to understand. More abstract models describing its behaviour may ease program understanding. Manually building understandable abstractions from complex source code - as an explicit or just mental model - requires in-depth analysis of the code in the first place. Therefore, it is a time-consuming and tedious activity for developers. Model mining can support program comprehension by semi-automatically extracting high-level models from code. One helpful model is a state machine, which is an established formalism for specifying the behaviour of a software component. In this paper, we report on a controlled experiment that investigates the question: Do semi-automatically extracted state machines make understanding of complex embedded code more effective? The experiment was conducted with 30 participants on two industrial embedded C code functions. The results show that the share of correct answers increases and the required time to solve the tasks decreases significantly when extracted state machines are available. We conclude that mined state machines do in fact help in program understanding.
Segmentation of hippocampus guided by assembled and weighted coherent point drift registration

Segmentation of the subcortical structures in the brain such as the hippocampus, is known to be very challenging owing to its’ image characteristics. In brain MR images, the hippocampus is observed as a gray matter structure that often exhibits very weak or unclear boundary definitions at some fragments of its’ boundary. The unclear boundaries even cause the medical experts to misjudge the hippocampus boundary, especially at the head and tail. In this research, an automated segmentation approach, termed as Assembled and Weighted Coherent Point Drift is investigated to delineate the hippocampus accurately. Evaluations on public datasets produced an average Dice Similarity Coefficient of 0.8050, which appears better, in comparison to several other hippocampus segmentation approaches, especially against the well-known software program called Freesurfer. The study also revealed that the accuracy of the proposed segmentation approach seems on par with other various state-of-the-art approaches.
Graph-based Density Peaks Ranking Approach for Extracting KeyPhrases (GDREK)

Surprisingly, there are more than 1,500,000 articles found by google scholar search engine on keyphrase extraction (KE) have been published recently, 21,000 of them only in current year. This large number implies that researchers need to find more accurate and better performing models for KE from text as a subtask of text mining and summarization. This paper presents a novel design of KE. The model is composed of Graph-based Representation, sentence clustering and ranking based on Density peaks for KE in single or multi-documents (GDREK) which can be used further in text extractive summarization. The principle of GDREK is using graph model to represent text and then group and rank the sentences in a mutuality manner. In this model, sentence grouping and ranking proceeds by discovering the main topics of text and finding central sentences of each topic incrementally. In this incremental step, as the sentences are grouped based on the Graph-based Growing Self-Organizing Map (G-GSOM), they are ranked using Density Peaks (DP) concept according to a measure of similarity between sentences. Our similarity measure is based on shared phrases and Cosine function. Sentences are scored under the assumption that when a sentence has more similar sentences, it is considered more important (higher density) and more representative. Finally, the most frequent words or phrases in the sentences are selected as key phrases of the text. Experimental results show that our innovative technique extracts the most key phrases and words of two datasets and yields over 75% accuracy and from most sub-topics of text.
Accelerated method for the optimization of quadratic image filter

Quadratic image filter involves the second-order multiplications of an input image mask in addition to linear terms, and determining the weights of the quadratic filter using optimization methods requires intense computational power due to the cost of the resulting fitness function. A graphics processing unit (GPU)-based algorithm is proposed to determine quadratic image filter weights using genetic algorithms and particle swarm optimization methods. Since the most time consuming part of determining the mask weights using heuristic algorithms is the fitness computation process, the fitness computation process is designed to work on the GPU, and a significant acceleration is achieved. For this purpose, different designs such as direct method and population-based method have been developed within the GPU to minimize training time. According to experimental results, the proposed methods provide significant accelerations over sequential implementation. The first method computes the fitness function for each population member separately, and it produces about 43 times acceleration over the sequential implementation. The second method developed is based on the complete evaluation of the population, and it produces about 151 times acceleration over the sequential implementation.
On the Effectiveness of Multi-Token Economies

Even though a token economy is the core part of a blockchain-based project, it is still in the early stage of academic exploration. Moreover, recent projects adopt multi-token economies to encourage the participants to stay longer in their economic systems, which are more complicated than single token economies. This paper addresses the token classification, the reason for adopting multi-token economies and the effectiveness of them. We analyze the Steemit as a representative example of multi-token economies. We describe how the multi-token economy has been working and show the distinctive features of multi-token economies. We also propose the evaluation criteria for multi-token economies.
Event Information Retrieval from Text

Events are an integral part of our day-to-day search needs. Users search for various kinds of events such as political events, organizational announcements, policy changes, personal events, criminal activity and so on. In linguistics, events are often thought of as discourse entities with associated complex structure and attributes.
Visualizing Literature Review Theme Evolution on Timeline Maps: Comparison Across Disciplines

Data-driven visualization techniques can be utilized to enhance the literature review process across different disciplines. In our work, 910 articles were retrieved using keyword search from bibliographic databases of two different disciplines (computer science: DBLP and medicine: MEDLINE) between 2001 and 2016. These articles' titles were processed using dynamic latent Dirichlet allocation to generate a set of themes/topics, which were subsequently classified and assigned to regions in a spatiotemporal geographical map. Resulting data visualizations from both repositories were manually reviewed by independent annotators. The results from the DBLP and MEDLINE were comparable and, taken together, suggest potential benefits of increased future interaction amongst multidisciplinary fields. Our findings indicate that spiral timeline maps have the potential to help researchers acquire or compare knowledge efficiently without prior domain knowledge.
Load Balancing Strategy in Cloud Computing Using Simulated Annealing

Cloud computing is an idea of diminution-based distributional model which depends on appropriate protocol over the internet. To meet the end user requirement here the resources like CPU, bandwidth and memory are provisioned and un-provisioned dynamically via an internet. To provide large scale computing infrastructure, these resources need to be scaled up and down dynamically on demand of the users in very flexible manner. The last decade records an aggressive growth in internet that is the reason cloud computing get a solid platform to expand its era. Though, it has a glorious future ahead but needs to resolve some problem. Load balancing is one of them. Load balancing is a mechanism to detect under loaded and overloaded node and distribute the load to balance them. To maintain the Quality of Service (QoS) cloud service provider (CSP) must have to satisfy the end users request on time for resources. For this reason, service providers must have to select a proper node that may complete user's task in between the deadline. So, providers must have to distribute a dynamic workload evenly across multiple nodes which ensure that no nodes are overloaded or under loaded which helps to reduce the average response time. It can be thought as an optimization problem and should have some adapting strategy to the changing needs. In this paper we propose a novel load balancing strategy to search under loaded node to balance load from overloaded one. Experimental results are really very encouraging of the sample application which is simulated by CloudAnalyst and the results of the proposed strategy are compared and outperformed the traditional strategy like First Come First Serve (FCFS), Round Robin (RR), local search algorithm like Stochastic Hill Climbing (SHC) and Genetic Algorithm (GA).
K-center algorithm for hierarchical binary template matching

To construct a hierarchical template tree of binary templates, the dissimilarity between templates was defined using distance transform, and a k-medoid algorithm was applied to select the representative of a template cluster. However, this method has a limitation in that the representative of a higher level cluster cannot exist in the space between the lower level clusters. In order to solve this problem, this paper proposes a k-center algorithm that finds a template that minimizes the distance to templates belonging to a cluster by using a genetic algorithm regarding each pixel of a binary template as a gene. The search space can be limited by randomly selecting the initial population from the templates belonging to the cluster. In particular, the weighted bidirectional distance is used as the dissimilarity between templates to prevent the cluster center from shrinking. By applying the proposed method to pedestrian silhouettes, it is possible to confirm that a leaf node similar to the given template is better detected and its performance is more stable.
Hahai: Computational Thinking in Primary Schools

Nowadays, society needs to keep up with the development of new technologies; the children of the new generation are seen as “digital natives”, although they are not necessarily able to use digital devices to their fullest potential. The “Hahai project” introduces the application of digital devices within the educational approach throughout the Hahai app. The aim of the project is to introduce digital skills and computational thinking through “Digital Storytelling” in primary schools; with the aim of developing problem-solving and the creative approach. Furthermore, these skills are extremely valuable in modern society and for the future. This paper explores the ideas behind the creation of the project, the development of the app, the testing in first-grade classes of primary school, the statistics obtained from the latter and the final conclusions.
Optical tomography for excited coherent states associated to deformed oscillators

In this manuscript, we study the deformed optical tomography for excited coherent states associated to deformed Heisenberg algebra. By using the numerical solution of the recurrence relation, we evaluate the eigenstates of the deformed quadrature operator and give the exact analytical formula of the optical tomogram for the photon-added deformed coherent states (PA-DCSs) and photon-added deformed cat coherent states (PA-DCCSs). Finally, we display the variation of the excited coherent states in the terms of the different physical parameters defined in the coherent states and show how these parameters affect the shape of the optimal tomography.
Mobile terminal trajectory recognition based on improved LSTM model

Mobile terminal hand trajectory recognition is challenged by spatio-temporal variations, complex background, light variations as well as the limited computing resources for mobile devices. The authors propose a hand trajectory recognition method based on improved Long-Short Term Memory network (LSTM) model. First, for the sake of eliminating the effects of complex background and light, the SSD hand detector and multi-color space are combined to segment gesture. To eliminate the influence of trajectory points' overlap, the sparseness of the trajectory points is used to determine the start and end of the trajectory; then, to maintain the feature difference while decreasing the feature extraction time, they extract the trajectory feature sequence based on the central point direction angle; Finally, a new loss function (M-Softmax loss) is proposed in LSTM training process to maximise the inter-class variations and minimise intra-class variations. At the same time, the adaptive training iteration number is proposed to ensure the convergence of the model and reduce the training time cost. The experimental results illustrate that the improved model learned features are more compact, and the model is easier to deal with external data changes. Authors’ proposed method has great significance in practical applications.