Reviewerly/exHarmony huggingface dataset:
IMPROVING THE INDONESIAN LANGUAGE SKILLS BY FOSTERING THE READING INTEREST OF 1ST GRADE STUDENTS

Abstract Language skills are one of the keys to success in improving learning as a means to preparing oneself to enter the world of information. But in reality, there are obstacles that make language learning become less optimal at school. The lack of time to learn Indonesian at school makes students less able to understand the material presented by the teacher and the time used by the teacher to explain the material, so the students' learning activities become passive and not creative. With these conditions, it is expected that teachers will prepare more interesting teaching materials, which can stimulate student learning activities so the learning can be more fun. Indonesian Language learning is the learning that emphasizes language skills as a means to learn science. Reading is one form of obtaining scientific information, so it is necessary to deepen understanding of the language. Therefore, it is necessary to use the best possible learning time allocation to encourage students to understand a reading. However, students' low interest in reading makes it difficult to be implemented. This can be seen from the analysis results of their reading interest that only reached the average of 63.4%. After conducting the research in cycles I and II, there began to be a change in the increase of students' reading motivation, this was obtained from the reflection results every time the research was done which averaged by 81.4%, which indicates an increase of 18%. Keywords : Reading Interest; Indonesian Language Skills
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization

We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarization of relevant article abstracts from systematic reviews previously conducted by members of the Cochrane collaboration, using the authors conclusions section of the review abstract as our target.1 We enlist medical professionals to evaluate generated summaries, and we find that summarization systems yield consistently fluent and relevant synopses, but these often contain factual inaccuracies. We propose new approaches that capitalize on domain-specific models to inform summarization, e.g., by explicitly demarcating snippets of inputs that convey key findings, and emphasizing the reports of large and high-quality trials. We find that these strategies modestly improve the factual accuracy of generated summaries. Finally, we propose a new method for automatically evaluating the factuality of generated narrative evidence syntheses using models that infer the directionality of reported findings.
Additional file 1 of Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis

Additional file 1 Most important TS features / alternative feature preselection algorithms. This file contains tables of the 20 most prominent TS features across the 1000 train/test splits, for both APB and AH anatomies. It also provides a way of obtaining the code used to generate these features. There is also a section on a few alternatives for the feature preselection step. Lastly the process of chosing hyperparameters is discussed and motivated.
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks

Knowledge transfer using convolutional neural networks (CNNs) can help efficiently train a CNN with fewer parameters or maximize the generalization performance under limited supervision. To enable a more efficient transfer of pretrained knowledge under relaxed conditions, we propose a simple yet powerful knowledge transfer methodology without any restrictions regarding the network structure or dataset used, namely self-supervised knowledge transfer (SSKT), via loosely supervised auxiliary tasks. For this, we devise a training methodology that transfers previously learned knowledge to the current training process as an auxiliary task for the target task through self-supervision using a soft label. The SSKT is independent of the network structure and dataset, and is trained differently from existing knowledge transfer methods; hence, it has an advantage in that the prior knowledge acquired from various tasks can be naturally transferred during the training process to the target task. Furthermore, it can improve the generalization performance on most datasets through the proposed knowledge transfer between different problem domains from multiple source networks. SSKT outperforms the other transfer learning methods (KD, DML, and MAXL) through experiments under various knowledge transfer settings. The source code will be made available to the public <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.
Study and Analysis of the Rare Word Handling Techniques for Medical Text Data

In today’s era, most of data on internet is in unstructured form in domains as finance, social media, medical, computer science, artificial intelligence, business, education and training. To mine text data, text summarization, feature extraction, classification, clustering, topic tracking, association rule mining and trend finding etc. methods available. Text classification is used to categorise data into pre-defined class/categories. The pr-processing steps, a part of classification process, include tokenization, stop words removal, stemming, word vector representation using binary bag-of-words, term frequency (TF), TF-IDF etc., feature selection and extraction. Text data processing has challenges like synonyms, polysemous/homonymous, common, low frequency etc. In this paper, the focus is one issue - rare terms/features (low frequency terms). Experiments show that low frequency terms are more informatics or having subjective knowledge related to domain. Accuracy of classifiers increase compare to all existing methods.
Diverse Plausible Shape Completions from Ambiguous Depth Images

We propose PSSNet, a network architecture for generating diverse plausible 3D reconstructions from a single 2.5D depth image. Existing methods tend to produce only small variations on a single shape, even when multiple shapes are consistent with an observation. To obtain diversity we alter a Variational Auto Encoder by providing a learned shape bounding box feature as side information during training. Since these features are known during training, we are able to add a supervised loss to the encoder and noiseless values to the decoder. To evaluate, we sample a set of completions from a network, construct a set of plausible shape matches for each test observation, and compare using our plausible diversity metric defined over sets of shapes. We perform experiments using Shapenet mugs and partially-occluded YCB objects and find that our method performs comparably in datasets with little ambiguity, and outperforms existing methods when many shapes plausibly fit an observed depth image. We demonstrate one use for PSSNet on a physical robot when grasping objects in occlusion and clutter.
Label-informed Graph Structure Learning for Node Classification

Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only consider feature information while ignoring available label information. In this paper, we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix. We conduct extensive experiments on seven node classification benchmark datasets and the results show that our method outperforms or matches the state-of-the-art baselines.
Blockchain Tools for Nuclear Safeguards

SLUMBAT and SLAFKA are a minimal demonstration and a prototype blockchain solution for nuclear material accounting. This chapter explains how these two demonstrations of DLT for nuclear safeguards met the requirements for the recording, reporting, and access control of information for nuclear material in two different States. The researchers’ reflections on developing and demonstrating these solutions are presented, followed by a discussion of what should be addressed for future iterations.
Topology-Optimized Metamaterials for Optical Signal Compression

Optical computing holds a significant position as a legitimate candidate to replace transistor-based computing as we near the end of silicon evolution. Here, for the first time, we unveil all-optical signal compression. The idea relies on wave-matter interaction in a careful inverse topology optimization of metamaterial structure. Inspired by digital image compression, we propose photonic signal compression based on multi-input multi-output metamaterial networks. The photonic metamaterial structure realizes the Haar wavelet transform for an array of beams as input signals and delivers an array of outputs with reduced entropy according to the Haar transform. Numerical demonstration of the photonic compressor is presented with inversely designed $4 \times 4$ network. Our photonic signal compressor may pave the way of replacing traditional digital signal and image processing devices with wave-based meta-structures.
Biological learning in key-value memory networks

In neuroscience, classical Hopfield networks are the standard biologically plausible model of long-term memory, relying on Hebbian plasticity for storage and attractor dynamics for recall. In contrast, memory-augmented neural networks in machine learning commonly use a key-value mechanism to store and read out memories in a single step. Such augmented networks achieve impressive feats of memory compared to traditional variants, yet their biological relevance is unclear. We propose an implementation of basic key-value memory that stores inputs using a combination of biologically plausible three-factor plasticity rules. The same rules are recovered when network parameters are meta-learned. Our network performs on par with classical Hopfield networks on autoassociative memory tasks and can be naturally extended to continual recall, heteroassociative memory, and sequence learning. Our results suggest a compelling alternative to the classical Hopfield network as a model of biological long-term memory.
A Platform for E-Learning of Traditional Three-Hole Wind Instruments

Tradition is the hallmark of every culture. This paper presents a traditional electronic instrument based on the three-hole flutes native to Spain, and specifically to Salamanca. The device has remote connection with smartphones, so it can send and receive information from an application developed for this device. To generate the different notes, it has the appropriate sensors to detect variations in air and fingering, and the smartphone app processes this information to generate the sound. This device seeks to promote the tradition, bringing it closer to new users and facilitating the rehearsal and creation of music by old users.
An Inference Accelerator Design for Sparse Convolution Neural Network

Convolution neural networks (CNN) is a widely used deep learning method at present. The weight and activation in the inference process of CNN have high sparsity. Making full use of the sparsity of CNN to accelerate is a research hotspot. This paper analyzes various classical accelerator structures, sparsity and network compression methods and sparsity accelerator structures. The weight pre compression storage method is used to explore the sparsity of weight in the inference process, and the aggregation-diffusion method is used to explore the sparsity of activation. A new sparse CNN inference accelerator structure is proposed, the complete design and implementation are carried out, and the analysis model of performance and overhead is established. The experimental results show that in terms of sparsity utilization, the proposed method can make use of 100% weight sparsity and more than 60% activation sparsity. In terms of circuit overhead, the proposed structure has strong scalability.
Dynamic Path Selection Based Video Transmission in User Preference Assisted Adaptive Rate Control in 5G Multi-RAT Network

5G mobile users consume a large amount of video content.Providing high-quality video to mobile users via a single path is a challenging task.It takes much time to transmit the video.In this paper, we proposed dynamic path selection-based video transmission is proposed for a 5G multi-RAT network.In our proposed system has four consecutive processes, which are discussed as follows: (1) Optimal access point selection which is done by Red Deer Algorithm (RDA) that selects optimal access point by considering data rate, Signal to Inference Noise Ratio (SINR) and Received Signal Strength Indicator(RSSI) to achieve better Quality of Experience (QoE).(2) Adaptive Video Encoding, for this purpose, we useH.265encoding algorithm, which encodes the video packets in order to reduce transmission time and bandwidth consumption, here bit rate is adaptively controlled using the SARSA reinforcement algorithm, by considering the network environment factors ( bit error rate, attenuation, bandwidth, throughput, and SNR) and user preference factors (high/low quality and processing speed).In this stage, the SWARA decision-making algorithm is used to select optimal QP parameters for each video packet, which considers three parameters: distortion, previous QP value, and CSI, which improves the quality of the video.(3).Dynamic path selection is made using the Deng-based Type 2 Fuzzy algorithm (Deng-T2F), which selects the optimal path between source and destination based on the following parameters: the number of hops, link stability, and buffer size increases high throughput and reduce transmission delay.(4).Adaptive Buffer Management is proposed for reducing latency during video transmission.The Adaptive Pre-order Deficit Round Robin (ADPDRR) algorithm is used to evaluate the parameters of layer information, deadline, packet size, and arrival time to reduce packet loss and packet waiting time during video transmission.The proposed APDRR algorithm maintains three queues based on the packet priority, and then the prioritized packets are transmitted adaptively to reduce the packet waiting time.Finally, simulation is conducted using an NS-3.26 network simulator that evaluates the performance based on the following metrics: PSNR, MoS, bandwidth utilization, jitter, Throughput, Delay, Packet drop rate, and Goodput.
Governance, CO2 emissions and Inclusive Human Development in Sub-Saharan Africa

This study investigates the relevance of government quality in moderating the incidence of environmental degradation on inclusive human development in 44 sub-Saharan African countries for the period 2000-2012. Environmental degradation is measured with CO2 emissions and the governance dynamics include: political stability, voice and accountability, government effectiveness, regulation quality, the rule of law and corruption-control. The empirical evidence is based on the Generalised Method of Moments. Regulation quality modulates CO2 emissions to exert a net negative effect on inclusive development. Institutional governance (consisting of corruption-control and the rule of law) modulates CO2 emissions to also exert a net negative effect on inclusive human development. Fortunately, the corresponding interactive effects are positive, which indicates that good governance needs to be enhanced to achieve positive net effects. A policy threshold of institutional governance at which institutional governance completely dampens the unfavourable effect of CO2 emissions on inclusive human development is established. Other policy implications are discussed.
Study on Nursing Effect of Individualized Health Education Combined with Cognitive Training in Elderly Stroke Patients

Objective To study the nursing effect of individualized health education combined with cognitive training in elderly stroke patients. Methods 112 elderly stroke patients treated in the Department of Neurology of our hospital from January to June 2017 were randomly divided into experimental group and control group. In the control group, routine treatment, nursing and rehabilitation exercises were carried out, and regular health education was carried out. The experimental group, on the basis of routine treatment, nursing and rehabilitation exercise, carried out individualized health education and cognitive training combining the content of individualized health education. The effect of intervention was evaluated by Montreal Cognitive Assessment Scale (MoCA), health education questionnaire, daily life self-care scale (ADL) and nurse job satisfaction questionnaire before intervention, at the end of intervention for 4 weeks and at the end of intervention for 12 weeks. Results At the end of 4th and 12th week after intervention, the scores o MoCA, the awareness rate of health education, ADL and job satisfaction of nurses were higher than those before intervention, and the observation group was higher than that of the control group (P&lt;0.05), with statistical difference (P&lt;0.05). Conclusion Individualized health education combined with cognitive training can effectively improve the patients’ cognitive function, improve the patients’ awareness of stroke disease and the compliance of health education, improve the patients’ self-care ability in daily life and the satisfaction of nursing work, so as to improve the patients’ quality of life and quality of living.
A2Cloud-H: A Multi-tiered Machine Learning Framework for Cost-Effective Cloud Resource Selection

We present the A2Cloud-Hierarchy (A2Cloud-H) framework that recommends Cloud instances to users for high-performance scientific computing. The framework comprises four components: training database, supervised learning (SL) module, unsupervised learning (USL) module, and a decision module. The training database contains the testing traces of previous applications and Cloud instances; these are contributed by the scientific community. The SL module comprises three popular supervised learning models: logistic regression, support vector machine, and random forest, which train using the database to qualitatively assess the instance performance for the target application. The USL module contains three collaborative filtering methods: application-based, instance-based, and rank-based, which use the database to estimate the instances’ performance ratings for the target application. The decision module includes multiple tiers of analytic hierarchy processing, which consolidate the instance recommendations from the SL and USL modules into a final instance recommendation. Our comprehensive testing with 20 Cloud instances and over 8 scientific applications yield more than 90% modeling accuracy. The framework’s ultimate goal is to encourage the scientific community to adopt the Cloud for green computing.
Administrative innovation and organisational justice across municipalities in Jordan: organisational commitment as a mediatory role

The role adopted by organisational justice (OJ) in line with administrative innovation (AI) is recognised as critical in terms of its influence on the viewpoints and performance of employees. Organ...
Probeable DARTS with Application to Computational Pathology

AI technology has made remarkable achievements in computational pathology (CPath), especially with the help of deep neural networks. However, the network performance is highly related to architecture design, which commonly requires human experts with domain knowledge. In this paper, we combat this challenge with the recent advance in neural architecture search (NAS) to find an optimal network for CPath applications. In particular, we use differentiable architecture search (DARTS) for its efficiency. We first adopt a probing metric to show that the original DARTS lacks proper hyperparameter tuning on the CIFAR dataset, and how the generalization issue can be addressed using an adaptive optimization strategy. We then apply our searching framework on CPath applications by searching for the optimum network architecture on a histological tissue type dataset (ADP). Results show that the searched network outperforms state-of-the-art networks in terms of pre-diction accuracy and computation complexity. We further conduct extensive experiments to demonstrate the transfer-ability of the searched network to new CPath applications, the robustness against downscaled inputs, as well as the reliability of predictions.
Cryptographic Algorithms and Protocols

Cryptography facilitates sharing of sensitive contents among the intended parties across any insecure network channels and prevents unintended recipients from knowing the secret information in the contents. Efforts are made to create tools for hiding information with modern cryptographic algorithms and security protocols. The technology is growing rapidly with public and private key cryptography for securing the sensitive information of organizations and performing online tasks with trusted parties. Many state-of-the-art cryptographic algorithms and protocols have been developed. For a promising researcher of this field, knowledge and understanding of the popular key algorithms and protocols is essential for developing a solid foundation for selecting a correct research direction. This chapter presents the key algorithms and protocols used in cryptography. The objective is to provide a technical description of key algorithms and protocols along with a basic understanding of the field.