Reviewerly/exHarmony huggingface dataset:
Optimization and Sizing of a Stand-Alone Photovoltaic System and Assessment of Random Load Fluctuation on Power Supply

This study presents an optimization technique and design for a stand-alone photovoltaic (PV) system to provide the required electricity for a single residential household in remote areas.From the basic solar components analysis, the irradiance on tilted surface is derived and compared to that on horizontal surface for Furu-Awa locality to infer the appropriate tilt angle (β) that maximizes the collection of solar energy.Seven optimum values of β applicable to the PV network were then derived depending of the period of the year and this simulation resulted that the panels are to be adjusted seven times a year.The optimization technique for load demand based on total apparent power of the household appliances produces an increase of 18% compared to the simple case of the PV components design using active power but leads to the optimum configuration that meets the real load demand of the household.Following the sizing of the station, reliability tests simulations were conducted for a one year corresponding period to infer the sensitivity of power supply to initial state of charge, to check the system autonomy and to evaluate the effect of random variation of the load on the smooth functioning of the PV system using a pseudo random number generator.This analysis shows that the minimum capacity of the battery for normal run of the Plan is 22.2% and that with random fluctuation of load, there will be periods of the year where the system experiences power failure depending on how important is the variation.The result of the study may imply a small increase in the cost of the entire plant but improves the stability and flexibility of such a station.
The Portuguese Maritime Voyages of Discovery: the exploration of the history of a city with an App as an educational resource

In this paper we present an evaluation of an App for mobile devices, ‘Roteiro dos Descobrimentos’, as an educational digital resource for primary school students. The study involved the participation of 131 students and eight teachers. Data were collected from participant observation, students’ questionnaires and interviews to students and teachers. According to students, they learned new things, related with the topics explored, in an easy and funny way. Students also emphasized as positive aspects the fact that they had to face different challenges and the need to mobilize their knowledge to solve them. Teachers referred that students showed great interest and enthusiasm during the activities. As main gains, teachers stressed that the application fosters the relationship of students with the city, facilitates collaboration, and promotes students’ autonomy. In resume, it seems that the playful and interactive dimension of the App promoted the development of important skills such as the ability to interact with the environment, collaborative work, autonomy, and reading and interpretation skills. As a conclusion, there is a great receptivity to integrate mobile technologies in the teaching and learning process, but the role of the teacher can’t be dismissed, as a mediator and educator.
Neural Architecture Search for Deep Image Prior

We present a neural architecture search (NAS) technique to enhance the performance of unsupervised image de-noising, in-painting and super-resolution under the recently proposed Deep Image Prior (DIP). We show that evolutionary search can automatically optimize the encoder-decoder (E-D) structure and meta-parameters of the DIP network, which serves as a content-specific prior to regularize these single image restoration tasks. Our binary representation encodes the design space for an asymmetric E-D network that typically converges to yield a content-specific DIP within 10-20 generations using a population size of 500. The optimized architectures consistently improve upon the visual quality of classical DIP for a diverse range of photographic and artistic content.
1-Point RANSAC UKF with Inverse Covariance Intersection for Fault Tolerance

The fault tolerance estimation method is proposed to maintain reliable correspondences between sensor data and estimation performance regardless of the number of valid measurements. The proposed method is based on the 1-point random sample consensus (RANSAC) unscented Kalman filter (UKF), and the inverse covariance intersection (ICI)-based data fusion method is added to the update process in the proposed algorithm. To verify the performance of the proposed algorithm, two analyses are performed with respect to the degree of measurement error reduction and accuracy of generated information. In addition, experiments are conducted using the dead reckoning (DR)/global positioning system (GPS) navigation system with a barometric altimeter to confirm the performance of fault tolerance in the altitude. It is confirmed that the proposed algorithm maintains estimation performance when there are not enough valid measurements.
EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles in urban environments, where occlusion is likely. For training, two datasets are used: the Urban Motorbike Dataset (UMD-10K) of 10,000 annotated images, and the new SMMD (Secretaría de Movilidad Motorbike Dataset), of 5,000 images captured from the Traffic Control CCTV System in Medellín (Colombia). Results achieved on the UMD-10K dataset reach 88.8% in average precision (AP) even when 60% motorcycles were occluded, and the images were captured from a low angle and a moving camera. Meanwhile, an AP of 79.5% is reached for SSMD. EspiNet V2 outperforms popular models such as YOLO V3 and Faster R-CNN (VGG16 based) trained end-to-end for those datasets
Adeft: Acromine-based Disambiguation of Entities from Text with applications to the biomedical literature

For machines to extract useful information from scientific documents, they must be able to identify the entities referenced in the text.For example, in the phrase "binding of ligand to the IR is reduced", "IR" refers to the insulin receptor, a gene with official symbol INSR.This process of identification, known as named entity disambiguation or grounding, requires the text string for the entity to be mapped to an identifier in a database or ontology.A complicating factor is that multiple distinct entities may be associated with the same text, leading to ambiguity.In scientific and technical documents, this ambiguity frequently originates from the use of overlapping acronyms or abbreviations: for example, in the biomedical literature, the term "IR" can refer not only to the insulin receptor, but also to ionizing radiation, ischemia reperfusion, insulin resistance, and other concepts.While interpreting these ambiguities is rarely a problem for human readers given the context of the whole document, it remains a challenge for text mining tools, many of which process text one sentence at a time.
Research on Obstacle Avoidance Strategy for Welding Robot

Abstract： Welding robots are widely used in industrial production because of their high efficiency, and collision detection is related to the safe production of robots.Aiming at the problem of obstacle avoidance between welding robot and workpiece, this paper uses the advantage of grid method to simplify the environment information, and uses the ant colony algorithm to calculate the speed of the obstacle avoidance path between each two solder joints.Find the collision-free path between the end of the robot and the workpiece.In the obstacle avoidance part of robot and fixture, this research uses the combination of rough detection and fine detection by the geometric modeling of the robot, the selection of the welding point position and the forward and reverse solution of the attitude.The collision detection completes the obstacle avoidance between the robot joint and the fixture.Finally, considering the welding gun attitude and fixture, according to the two principles of the same attitude and welding time, the group competitive particle swarm optimization algorithm is used to optimize the welding time of the robot, and the welding point division and path planning of the double welding robot are realized.The robot has the best welding time target.
Brief Paper: Vehicle Manufacturer Recognition using Deep Learning and Perspective Transformation

In real world object detection is an active research topic for understanding different objects from images.There are different models presented in past and had significant results.In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN).Both the front and rear view of the vehicles were used for training and testing the proposed method.Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images.Using perspective transformation, the top view images were transformed into front view images.This algorithm has higher detection rate as compared to raw images.Furthermore, YOLO model has better result as compare to F-RCNN model.
Playful interfaces to the archive and the embodied experience of data

Purpose The purpose of this paper is to demonstrate the possibility for the galleries, libraries, archives and museums sector to employ playful, immersive discovery interfaces for their collections and raise awareness of some of the considerations that go into the decision to use such technology and the creation of the interfaces. Design/methodology/approach This is a case study approach using the methodology of research through design. The paper introduces two examples of immersive interfaces to archival data created by the authors, using these as a springboard for discussing the different kinds of embodied experiences that users have with different kinds of immersion, for example, the exploration of the archive on a flat screen, a data “cave” or arena, or virtual reality. Findings The role of such interfaces in communicating with the audience of an archive is considered, for example, in allowing users to detect structure in data, particularly in understanding the role of geographic or other spatial elements in a collection, and in shifting the locus of knowledge production from individual to community. It is argued that these different experiences draw on different metaphors in terms of users’ prior experience with more well-known technologies, for example, “a performance” vs “a tool” vs “a background to a conversation”. Originality/value The two example interfaces discussed here are original creations by the authors of this paper. They are the first uses of mixed reality for interfacing with the archives in question. One is the first mixed reality interface to an audio archive. The discussion has implications for the future of interfaces to galleries, archives, libraries and museums more generally.
IoT Application in Sports to Support Skill Acquisition and Improvement

This paper proposes a sport-service-oriented architecture framework and describes an example of design and implementation of an IoT application in sports based on sport-skills researches. Especially, the focus is on wearable applications with inertial sensors because sports applications require simple and durable motion-sensing systems. Skateboarding is introduced as a target sport to understand skill components and to support skill acquisition and improvement. Two types of skills (i.e., basic and advanced) are introduced to show different requirements for IoT applications in sports. Design factors to implement a sports application are discussed in terms of two aspects (i.e., analysis and service phases) concerning sports activities. To implement these designs, technology selection and implementation alternatives are discussed in terms of skill types and purpose of sensing (i.e., analysis or service phase).
Month of the year effect in the cryptocurrency market and portfolio management

Purpose – to investigate the Month of the year effect in the cryptocurrency market.&#x0D; Design/Method/Research Approach. A number of parametric and non-parametric technics are used, including average analysis, Student's t-test, ANOVA, Kruskal-Wallis statistic test, and regression analysis with the use of dummy variables.&#x0D; Findings. In general (case of overall testing – when all data is analyzed at once) calendar the Month of the Year Effect is not present in the cryptocurrency market. But results of separate testing (data from the period “suspicious for being anomaly” with all the rest of the data, except the values which belong to the “anomaly data set”) shows that July and August returns are much lower than returns on other months. These are the worst months to buy Bitcoins.&#x0D; Theoretical implications. Results of this paper claim to find some holes in the efficiency of the cryptocurrency market, which can be exploited. This contradicts the Efficient Market Hypothesis.&#x0D; Practical implications. Results of this paper claim to find some holes in the efficiency of the cryptocurrency market, which can be exploited. This provides opportunities for effective portfolio management in the cryptocurrency market.&#x0D; Originality/Value. This paper is the first to explore Month of the Year Effect in the cryptocurrency market.&#x0D; &#x0D; Paper type – empirical.&#x0D; &#x0D; Authors gratefully acknowledge financial support from the Ministry of Education and Science of Ukraine (0117U003936).
Dot Matrix OCR for Bottle Validity Inspection

Identifying expiration dates on water bottles for fast industrial processing of a large amount of products mainly affects water bottling factories, food warehouses and supermarkets. The impact of this problem is the distribution of expired bottles of water or their existence on the market. One of the key issues for automatic character readers from bottles is dotted text. Furthermore, the transparent and curved nature of the containing bottle in the presence of water increases the difficulty of the text extraction. An optical character recognition solution using a convolutional neural networks is proposed to solve this issue. The proposed solution segments the input image to extract the bottle, detects the text region of interest, then performs pre-processing operations and finally converts the characters extracted from the region of interest on the bottle in human-readable characters. The proposed solution has real time performance and it achieves high quality results on the evaluation set.
Double-port measurements for robust quantum optical metrology

It has been proposed and demonstrated that path-entangled Fock states (PEFSs) are robust against photon loss over NOON states [S. D. Huver et al., Phys. Rev. A 78, 063828 (2008)]. However, the demonstration was based on a measurement scheme which was yet to be implemented in experiments. In this work, we quantitatively illustrate the advantage of PEFSs over NOON states in the presence of photon losses by analytically calculating the quantum Fisher information. To realize such an advantage in practice, we then investigate the achievable sensitivities by employing three types of feasible measurements: parity, photon-number-resolving, and homodyne measurements. We here apply a double-port measurement strategy where the photons at each output port of the interferometer are simultaneously detected with the aforementioned types of measurements.
Multi-step Joint-Modality Attention Network for Scene-Aware Dialogue System

Understanding dynamic scenes and dialogue contexts in order to converse with users has been challenging for multimodal dialogue systems. The 8-th Dialog System Technology Challenge (DSTC8) proposed an Audio Visual Scene-Aware Dialog (AVSD) task, which contains multiple modalities including audio, vision, and language, to evaluate how dialogue systems understand different modalities and response to users. In this paper, we proposed a multi-step joint-modality attention network (JMAN) based on recurrent neural network (RNN) to reason on videos. Our model performs a multi-step attention mechanism and jointly considers both visual and textual representations in each reasoning process to better integrate information from the two different modalities. Compared to the baseline released by AVSD organizers, our model achieves a relative 12.1% and 22.4% improvement over the baseline on ROUGE-L score and CIDEr score.
Computer-Assisted Pap Test Screening

Two FDA-approved automated imaging systems are in widespread use today, namely the ThinPrep Imaging System and FocalPoint GS Imaging System. Both systems use proprietary algorithms applied to computerized images. Post-implementation studies have shown that this technology improves cytologists’ productivity in high-volume cytology laboratories. However, several studies have questioned the impact on workload, cost effectiveness, and accuracy of these automation-associated systems compared to manual screening. This chapter reviews the development, underlying technology, and workflow of various automated Pap test screening systems. The literature regarding automated Pap test screening is also critically reviewed.
Time optimal control based on classification of quantum gates

We study the minimum time to implement an arbitrary two-qubit gate in two heteronuclear spins systems. We give a systematic characterization of two-qubit gates based on the invariants of local equivalence. The quantum gates are classified into four classes, and for each class the analytical formula of the minimum time to implement the quantum gates is explicitly presented. For given quantum gates, by calculating the corresponding invariants one easily obtains the classes to which the quantum gates belong. In particular, we analyze the effect of global phases on the minimum time to implement the gate. Our results present complete solutions to the optimal time problem in implementing an arbitrary two-qubit gate in two heteronuclear spins systems. Detailed examples are given to typical two-qubit gates with or without global phases.
A tutorial on the range variant of asymmetric numeral systems

This paper is intended to be a brief and accessible introduction to the range variant of asymmetric numeral systems (ANS), a system for lossless compression of sequences which can be used as a drop in replacement for arithmetic coding (AC). Because of the relative simplicity of ANS, we are able to provide enough mathematical detail to rigorously prove that ANS attains a compression rate close to the Shannon limit. Pseudo-code, intuitive interpretation and diagrams are given alongside the mathematical derivations. A working Python demo which accompanies this tutorial is available at https://raw.githubusercontent.com/j-towns/ans-notes/master/rans.py.
Blockchain moderated by empty blocks to reduce the energetic impact of crypto-moneys

While cryptocurrencies and blockchain applications continue to gain popularity, their energy cost is evidently becoming unsustainable. In most instances, the main cost comes from the required amount of energy for the Proof-of-Work, and this cost is inherent to the design. In addition, useless costs from discarded work (e.g., the so-called Forks) and lack of scalability (in number of users and in rapid transactions) limit their practical effectiveness. In this paper, we present an innovative scheme which eliminates the nonce and thus the burden of the Proof-of-Work which is the main cause of the energy waste in cryptocurrencies such as Bitcoin. We prove that our scheme guarantees a tunable and bounded average number of simultaneous mining whatever the size of the population in competition, thus by making the use of nonce-based techniques unnecessary, achieves scalability without the cost of consuming a large volume of energy. The technique used in the proof of our scheme is based on the analogy of the analysis of a green leader election. The additional difference with Proof-of-Work schemes (beyond the suppression of the nonce field that is triggering most of the waste), is the introduction of (what we denote as) "empty blocks" which aim are to call regular blocks following a staircase set of values. Our scheme reduces the risk of Forks and provides tunable scalability for the number of users and the speed of block generation. We also prove using game theoretical analysis that our scheme is resilient to unfair competitive investments (e.g., "51 percent" attack) and block nursing.
Convolutional Neural Networks Architectures for Facial Expression Recognition

This paper presents an overview of some convolutional neural networks architectures and methods for solving the facial expression recognition task using the FER_2013 image dataset. In this sense, three different convolutional networks have been trained and analyzed: a simple sequential architecture, a very lightweight network inspired from the XCEPTION architecture, and the well-known ResNet50 model. Following the experiments conducted using the above-mentioned architectures we have managed to obtain a state-of-the art comparable, 71.25% prediction accuracy on the test subset of FER-2013.
Distance metric learning for graph structured data

Graphs are versatile tools for representing structured data. As a result, a variety of machine learning methods have been studied for graph data analysis. Although many such learning methods depend on the measurement of differences between input graphs, defining an appropriate distance metric for graphs remains a controversial issue. Hence, we propose a supervised distance metric learning method for the graph classification problem. Our method, named interpretable graph metric learning (IGML), learns discriminative metrics in a subgraph-based feature space, which has a strong graph representation capability. By introducing a sparsity-inducing penalty on the weight of each subgraph, IGML can identify a small number of important subgraphs that can provide insight into the given classification task. Because our formulation has a large number of optimization variables, an efficient algorithm that uses pruning techniques based on safe screening and working set selection methods is also proposed. An important property of IGML is that solution optimality is guaranteed because the problem is formulated as a convex problem and our pruning strategies only discard unnecessary subgraphs. Furthermore, we show that IGML is also applicable to other structured data such as itemset and sequence data, and that it can incorporate vertex-label similarity by using a transportation-based subgraph feature. We empirically evaluate the computational efficiency and classification performance of IGML on several benchmark datasets and provide some illustrative examples of how IGML identifies important subgraphs from a given graph dataset.
Reduced Complexity Recursive Grassmannian Quantization

We propose a novel recursive multi-stage approach to Grassmannian quantization. Compared to the commonly employed single-stage quantization, our method has the advantage of significantly decreasing the number of codebook searches required for quantization and, thus, reducing the complexity. On the downside, the multi-stage approach causes a slight rate-distortion degradation compared to single-stage quantization. We analyze the rate-distortion performance of the proposed recursive quantization approach, considering random vector quantization within the individual stages. We furthermore propose a bit-allocation optimization amongst the stages of the quantizer, given a constraint on the total number of quantization bits.