Reviewerly/exHarmony huggingface dataset:
An enhanced KNN-based twin support vector machine with stable learning rules

Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers on the output model. (2) An extra stabilizer term was added to each objective function. As a result, the learning rules of the proposed method are stable. (3) To reduce the computational cost of finding KNNs for all the samples, location difference of multiple distances based k-nearest neighbors algorithm (LDMDBA) was embedded into the learning process of the proposed method. The extensive experimental results on several synthetic and benchmark datasets show the effectiveness of our proposed RKNN-TSVM in both classification accuracy and computational time. Moreover, the largest speedup in the proposed method reaches to 14 times.
MAVBench: Micro Aerial Vehicle Benchmarking

Unmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitous in everyday life. Among them, Micro Aerial Vehicles (MAVs) have seen an outburst of attention recently, specifically in the area with a demand for autonomy. A key challenge standing in the way of making MAVs autonomous is that researchers lack the comprehensive understanding of how performance, power, and computational bottlenecks affect MAV applications. MAVs must operate under a stringent power budget, which severely limits their flight endurance time. As such, there is a need for new tools, benchmarks, and methodologies to foster the systematic development of autonomous MAVs. In this paper, we introduce the `MAVBench' framework which consists of a closed-loop simulator and an end-to-end application benchmark suite. A closed-loop simulation platform is needed to probe and understand the intra-system (application data flow) and inter-system (system and environment) interactions in MAV applications to pinpoint bottlenecks and identify opportunities for hardware and software co-design and optimization. In addition to the simulator, MAVBench provides a benchmark suite, the first of its kind, consisting of a variety of MAV applications designed to enable computer architects to perform characterization and develop future aerial computing systems. Using our open source, end-to-end experimental platform, we uncover a hidden, and thus far unexpected compute to total system energy relationship in MAVs. Furthermore, we explore the role of compute by presenting three case studies targeting performance, energy and reliability. These studies confirm that an efficient system design can improve MAV's battery consumption by up to 1.8X.
The Effect of Flipped Learning in ‘Photosynthesis’ Unit of Middle School Science

In this study, the effect of the flipped learning on the science achievement and science learning motivation, and perception of flipped learning were examined in ‘photosynthesis’ unit of middle school science. In the class of
Optimized Blockchain Model for Internet of Things based Healthcare Applications.

There continues to be a recent push to taking the cryptocurrency based ledger system known as Blockchain and applying its techniques to non-financial applications. One of the main areas for application remains Internet of Things (IoT) as we see many areas of improvement as we move into an age of smart cities. In this paper, we examine an initial look at applying the key aspects of Blockchain to a health application network where patients health data can be used to create alerts important to authenticated healthcare providers in a secure and private manner. This paper also presents the benefits and also practical obstacles of the blockchain-based security approaches in IoT.
Application Development of Modern Multimedia Technology in English Teaching

With the rapid development of information technology in China in recent years, the data resources related to learning on the network platform have gradually increased. As of now, the network platform has become the largest information database in the world and is widely used in various fields. This paper expands the current situation of the use of modern multimedia technology in English teaching under the background of Internet information, and proposes countermeasures to rationally apply multimedia technology in English teaching to improve the overall quality of English teaching.
A Theory of Genetic Interests

For several decades now, jurists have struggled to adapt the law to questions raised by genetic science. They have done so primarily by seeking to fit these new questions into existing legal categories. These efforts have intensified as uses of genetics have expanded in areas such as criminal law enforcement, genealogy, genetic testing and screening (including prenatally), and the development of genetics-based medical therapies, including stem cell technologies, gene therapies, immunotherapies, and more. But genetics fits poorly into existing legal frames. Genetic materials are at the same time chemical, functional, and informational entities. They are widely shared and intimately individual, fixed and everchanging, and they are rife with meanings that keep evolving as our knowledge and understanding of genetic science broadens and deepens. Efforts to capture some aspects of genetic materials under one legal category inevitably leave out other aspects, and legal approaches taken in one area are often inconsistent with approaches taken in others. In sum, genetic materials and the information they encompass are uniquely multifaceted in ways that cannot be adequately captured under existing bodies of law.In this article we identify the shortcomings of existing efforts to capture genetics within the law and suggest an alternative approach that is grounded in the multifaceted nature of genetic materials. We begin by elucidating the multitude of aspects of genetic materials and information: chemical-physical, informational, structural-functional, reproductive, taxonomical, and identity-conferring. We then illustrate why existing legal constructs — e.g., under property law, privacy law, tort law, patent law, criminal law, and even constitutional law — fail to adequately capture the full range of interests that individuals, families, and society at large have in genetic materials and information. We conclude with the case for a new holistic legal approach to genetics that would encompass the multidimensional nature of genetic materials and information.
Locutionary act in motivating students at SMPN 2 Wungu

The aim of this research is to explain the speech act which is used to motivate students at SMPN 2 Wungu. The objective of this research is to explain the type of locutionary act which is used to motivate students. The research approach is qualitative method using observation and interview to collect the data. In this research, the researcher takes three teachers for source of data. Data and method triangulation are used to perform the data validity. Data are analyzed under interactive model. The result of this analysis are there are 26 (52%) utterances that belong to indirect speech act and 24 (48%) utterances belong to direct speech act, thus, it can be concluded that the type which is used to motivate students at SMPN 2 Wungu is indirect speech act.
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference

In this work, we propose to quantize all parts of standard classification networks and replace the activation-weight--multiply step with a simple table-based lookup. This approach results in networks that are free of floating-point operations and free of multiplications, suitable for direct FPGA and ASIC implementations. It also provides us with two simple measures of per-layer and network-wide compactness as well as insight into the distribution characteristics of activationoutput and weight values. We run controlled studies across different quantization schemes, both fixed and adaptive and, within the set of adaptive approaches, both parametric and model-free. We implement our approach to quantization with minimal, localized changes to the training process, allowing us to benefit from advances in training continuous-valued network architectures. We apply our approach successfully to AlexNet, ResNet, and MobileNet. We show results that are within 1.6% of the reported, non-quantized performance on MobileNet using only 40 entries in our table. This performance gap narrows to zero when we allow tables with 320 entries. Our results give the best accuracies among multiply-free networks.
Contextual Compositionality Detection with External Knowledge Bases andWord Embeddings

When the meaning of a phrase cannot be inferred from the individual meanings of its words (e.g., hot dog), that phrase is said to be non-compositional. Automatic compositionality detection in multi-word phrases is critical in any application of semantic processing, such as search engines; failing to detect non-compositional phrases can hurt system effectiveness notably. Existing research treats phrases as either compositional or non-compositional in a deterministic manner. In this paper, we operationalize the viewpoint that compositionality is contextual rather than deterministic, i.e., that whether a phrase is compositional or non-compositional depends on its context. For example, the phrase `green card' is compositional when referring to a green colored card, whereas it is non-compositional when meaning permanent residence authorization. We address the challenge of detecting this type of contextual compositionality as follows: given a multi-word phrase, we enrich the word embedding representing its semantics with evidence about its global context (terms it often collocates with) as well as its local context (narratives where that phrase is used, which we call usage scenarios). We further extend this representation with information extracted from external knowledge bases. The resulting representation incorporates both localized context and more general usage of the phrase and allows to detect its compositionality in a non-deterministic and contextual way. Empirical evaluation of our model on a dataset of phrase compositionality, manually collected by crowdsourcing contextual compositionality assessments, shows that our model outperforms state-of-the-art baselines notably on detecting phrase compositionality.
ANALISIS MODEL REGRESI SEDIMEN KOLAM LELE, SUKROSA, DAN BIOFERTILIZER TERHADAP PROSES NITRIFIKASI

Response Surface Methodology (RSM) was used to create regression model for sediment of catfish ponds, sucroses, and biofertilizers on the nitrification process.The sediment of catfish pond used was sediment collected from catfish farm at age of 21 days after seed stocking.The nitrification process was done in active sludge reactor of 1 L reactor working capacity which was entirely given ammonia in the form of NH3-N as much as 77.65 ppm.Central Composite Design (CCD) was used to determine the amount of sediment, sucrose, and biofertilizer.The ammonia nitrification process was carried out over a period of 21 days.The three main parameters, including rate of ammonia oxidation (NH3-N), nitrite accumulation (NO2-N), and nitrate accumulation (NO3-N).From this research, it was known that 74 g/L sediments of catfish ponds, 6.6 g/L sucrose 7.5 mL/L biofertilizer were required to obtain maximum ammonia oxidation rate.The result of the verification experiment obtained was 99.85%.In this research, two isolates of bacteria were isolated.Biochemical test using kit of VITEK GN+ (bioMerieux) indicated that the isolated bacteria were belong to the species of Sphingobacterium thalpophilum (probability = 99%) and Cupriavidus pauculus (probabililty = 98%).
Penggunaan Media Slide Powerpoint dalam Meningkatkan Prestasi Belajar Siswa Sekolah Dasar Pada Pembelajaran Tematik

The purpose of this study was to improve the quality of learning and learning achievement of grade IV students in my Heroes' Theme using PowerPoint slide media. This research was conducted in two cycles. Where in each cycle has stages that start from doing planning, action, observation and reflection. The results of the study: (1) through the application of PowerPoint slide media can improve the quality of learning; (2) the use of PowerPoint slide media can improve learning achievement 'pahlawanku' Theme in grade IV of SDN Sanan 1. The average value of the class before being subjected to action is 62, in cycle 1 it increases to 71 and in cycle II to 80. The number of students completing study at pre-action as many as 5 students or equal to 35.7%, in the first cycle increased to 9 students or equal to 64.3%, and in the second cycle to 12 students or by 85.7%. The advice given is that the teacher should use innovative models and varied media in learning.
Genetic Feature Construction Genetic Feature Construction: a parallel implementation of a genetic programming tool for feature construction

&#x0D; In this paper a parallel implementation of a recently introduced method for feature construction is described. This technique utilizes parallel genetic algorithms along with RBF neural networks to create new features from the original ones by discovering the hidden relations between patterns. The method is tested on series of classification problems from a variety of areas and the results are reported. The accompanied software is written entirely in ANSI C++ using the well established MPI library for parallelization.&#x0D;
Visual Object Tracking Using Deep Similarity Networks

Le suivi d’objets est une des tâches fondamentales de la vision par ordinateur. Etant donne une cible initiale dans la premiere trame, l’objectif du suivi est de trouver la nouvelle position de la cible dans les trames subsequentes. Les filtres de correlation est une technique largementutilisee dans les applications de reconnaissance de formes pour mettre en correspondance des objets. Ainsi, de nombreuses methodes de suivi sont basees sur les filtres de correlation. Bien que ces methodes de suivi atteignent un bon compromis entre la precision et la vitesse de suivi, leurs performances sont limitees par l’utilisation de caracteristiques d’apparence definies manuellement. Recemment, les methodes d’apprentissage profond ont demontre des resultats impressionnants dans diverses tâches de vision par ordinateur. De ce fait, un grand nombre de methodes de suivi basees sur l’apprentissage profond ont ete proposees pour ameliorer la robustesse et la capacite a discriminer les objets durant le suivi. Cependant, la representationdes caracteristiques de la cible par un seul reseau neuronal convolutif (RNC) n’est souvent pas assez discriminante. De plus, l’architecture du RNC doit etre choisie avec soins pour reduire le temps de calcul lors du suivi. Dans notre travail, nous avons explore differentes methodes pour ameliorer le pouvoir discriminant d’un RNC pour le suivi d’un objet. Nous avons propose deux methodes de suivi, MBST (Multi-Branch Siamese Tracker) et MFST (Multiple Features-Siamese Tracker). Les deux methodes sont basees sur des RNC avec une architecture siamoise. Cette architecture definit le suivi comme un probleme d’apprentissage par similarite. ----------ABSTRACT : In this thesis, we study visual object tracking using deep similarity networks. Visual objecttracking is a fundamental task in computer vision. Many approaches based on correlation filters and deep learning have been proposed to solve this problem. Inspired by deeplearning-based methods, we exploit the siamese network model to address object tracking, by formulating tracking as a similarity learning problem. Most deep learning methods only use features extracted from the last convolutional layer ofa single model which lead their tracker to be prone to fail when target appearance changes significantly. Different features extracted from different convolutional models encode the target object in different ways. Trackers with diverse feature representations can be adapted more easily to challenging scenarios. Besides, the discriminative power of features from the last convolutional layer is very limited. Since convolutional features from different layerscontain different levels of abstraction of the target object, discovering an appropriate scheme to fuse hierarchical features is also beneficial for tracking.
Proposal of Leaving the Bed Motion Detection Method Corresponded at Night using Obrid-Sensor

Recently, due to the aging of population (1) , the increasing of dementia patients has become social problem. The dementia patients are feared to go wander, and some of them may be gone missing after going out and have a risk to encounter accident (2) . However, watching over them all the time is difficult. Especially watching over at midnight or after sleeping is mental burden for helpers. In order to solve the problem, watching by camera is given, but it is difficult to use cameras in daily life from the viewpoint of privacy violation. As the solution for this problem, we have developed one-dimensional brightness distribution sensor (Obrid-Sensor) that can detect the subject's position, motion and condition without violating their privacy. In this paper, the method of leaving the bed detection that detect the leaving the bed motion day and night by installing the sensor in the edge of in the bed and irradiating infrared light is proposed.
Exploring the context of recurrent neural network based conversational agents

Conversational agents have begun to rise both in the academic (in terms of research) and commercial (in terms of applications) world. This paper investigates the task of building a non-goal driven conversational agent, using neural network generative models and analyzes how the conversation context is handled. It compares a simpler Encoder-Decoder with a Hierarchical Recurrent Encoder-Decoder architecture, which includes an additional module to model the context of the conversation using previous utterances information. We found that the hierarchical model was able to extract relevant context information and include them in the generation of the output. However, it performed worse (35-40%) than the simple Encoder-Decoder model regarding both grammatically correct output and meaningful response. Despite these results, experiments demonstrate how conversations about similar topics appear close to each other in the context space due to the increased frequency of specific topic-related words, thus leaving promising directions for future research and how the context of a conversation can be exploited.
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Predicting properties of nodes in a graph is an important problem with applications in a variety of domains. Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest of the nodes in the graph. Recently, Graph Convolutional Networks (GCNs) have achieved impressive performance on the graph-based SSL task. In addition to label scores, it is also desirable to have confidence scores associated with them. Unfortunately, confidence estimation in the context of GCN has not been previously explored. We fill this important gap in this paper and propose ConfGCN, which estimates labels scores along with their confidences jointly in GCN-based setting. ConfGCN uses these estimated confidences to determine the influence of one node on another during neighborhood aggregation, thereby acquiring anisotropic capabilities. Through extensive analysis and experiments on standard benchmarks, we find that ConfGCN is able to outperform state-of-the-art baselines. We have made ConfGCN's source code available to encourage reproducible research.
Distant Learning for Entity Linking with Automatic Noise Detection

Accurate entity linkers have been produced for domains and languages where annotated data (i.e., texts linked to a knowledge base) is available. However, little progress has been made for the settings where no or very limited amounts of labeled data are present (e.g., legal or most scientific domains). In this work, we show how we can learn to link mentions without having any labeled examples, only a knowledge base and a collection of unannotated texts from the corresponding domain. In order to achieve this, we frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels. As the learning signal is weak and our surrogate labels are noisy, we introduce a noise detection component in our model: it lets the model detect and disregard examples which are likely to be noisy. Our method, jointly learning to detect noise and link entities, greatly outperforms the surface matching baseline. For a subset of entity categories, it even approaches the performance of supervised learning.
Using Category Theory in Modeling Generics in OOP (Outline)

Modeling generics in object-oriented programming languages such as Java and C# is a challenge. Recently we proposed a new order-theoretic approach to modeling generics. Given the strong relation between order theory and category theory, in this extended abstract we present how also some tools from category theory, such as adjunctions, monads and operads, are used in our approach to modeling generics.
Clothoidal Local Path Template for Intention Estimation by Assistive Mobile Robots

This work proposes and evaluates various improvements to a circular local path template (LPT) that we have designed in the past to estimate driver intents and to provide navigation assistance to wheelchair drivers. This LPT may also be useful in other mobile robotics applications such as for intuitive teleoperated control, for fast collision checking in path planning or for obstacle avoidance algorithms. The LPT consists of a large but fixed set of paths in the mobile robot’s local neighborhood. Based on an efficient look-up table, the LPT paths’ lengths are adjusted such that they are collisionfree. However, experiments have shown that in dense environments insufficient circular paths are found, which impedes correct intention estimation and thus navigation assistance. In this work, the use of clothoidal paths rather than circular paths is evaluated. This substantially improves the capability to find complex paths in dense areas. Furthermore, we adapt the LPT to deal with dynamic obstacles of random shape using motion prediction estimates of these objects.