Reviewerly/exHarmony huggingface dataset:
Surface Recognition using a Composite Augmented Reality Marker

This reserch newly proposes an augmented reality system called "composite marker" to recognize curved objects obstructing within mobile robot workspace. Fundamental experiments are carried out to identify a sphere to verify its effectiveness.
IPTV User QoE Prediction based on Broad Learning System

With the rapid development of Internet technology, users’ demands for Internet Protocol Television (IPTV) service quality continue to increase. Quality of experience (QoE) prediction has become an urgent problem to be solved because QoE is an important indicator for the evaluation of IPTV services. However, with the sharp increase in the amount of data, QoE is difficult to effectively predict using traditional machine learning methods. For deep neural networks, it may suffer from time-consuming training process due to a great number of parameters and complicated structures. In order to obtain the availability and efficiency of QoE prediction, this paper proposes an IPTV user QoE prediction scheme based on Broad Learning System (BLS). The designed BLS scheme contains three steps. Firstly, the collected IPTV dataset is preprocessed to remove abnormal data and useless fields. Secondly, network related parameters as well as user pause behavior, respectively belonging to objective and subjective influencing factors, are used for feature extraction. Subsequently, the extracted features are entered into the BLS for training, and finally the trained model is used to realize QoE prediction. Experimental results show that the proposed scheme is more effective and efficient for QoE prediction compared with competing models.
Character-Aware Decoder for Translation into Morphologically Rich Languages

Neural machine translation (NMT) systems operate primarily on words (or sub-words), ignoring lower-level patterns of morphology. We present a character-aware decoder designed to capture such patterns when translating into morphologically rich languages. We achieve character-awareness by augmenting both the softmax and embedding layers of an attention-based encoder-decoder model with convolutional neural networks that operate on the spelling of a word. To investigate performance on a wide variety of morphological phenomena, we translate English into 14 typologically diverse target languages using the TED multi-target dataset. In this low-resource setting, the character-aware decoder provides consistent improvements with BLEU score gains of up to $+3.05$. In addition, we analyze the relationship between the gains obtained and properties of the target language and find evidence that our model does indeed exploit morphological patterns.
Collaborative Multi-key Learning with an Anonymization Dataset for a Recommender System

Balancing accuracy and privacy is an important tradeoff problem for information systems, including recommender systems. To achieve high performance, modern recommender systems tend to use as much information as possible. This trend is borne out by the increasing number of studies of hybrid methods that combine rating and auxiliary information. However, because of privacy concerns, in many cases, service providers can not require users to give their personal information. Therefore, numerous earlier reported methods only use item attributes for auxiliary information. To address these shortcomings, our manuscript provides a method to extract user profiles without using demographic data. Our model learns user and item latent variables through two separate deep neural networks and also learns implicit relations between users and items using the information and their ratings. Experiments verified that our model is a more effective recommender system than state-of- the-art baselines.
A reader response approach in collaborative reading projects to foster critical thinking skills

Reading has become a major concern of EFL educators.  Reading does not only help students learn foreign languages, but it is believed that it has a strong link with critical thinking skills. A reader response approach in collaborative works, adapted from literary theory, is believed to be beneficial for the students. Therefore, this study aims at investigating the answers to these two questions: (1) how are the collaborative reader responses implemented in Critical Reading and Writing II? and (2) To what extent does reader response approaches promote students’ critical thinking skills? With these questions in mind, the researcher collects the data by involving 24 participants from CRW II (Critical Reading and Writing) class. The data from students’ reflections, questionnaires, and focus group discussion are analyzed descriptively, using both qualitative and quantitative method. It is hoped that the implementation of this approach can be useful not only to improve students’ reading skills, but it provides more opportunity for students to exercise their critical thinking skills.
Design and Analysis of a Strengthen Internal Control Scheme for Smart Trust Financial Service

The construction of the financial module is very important in the intelligent management of the company. The collection, transmission, and processing of relevant item information in the financial module are of great significance to the construction of the intelligent financial module. The intelligent financial processing module can improve financial office efficiency and increase the security of the financial processing process. Section 404 of the Sarbanes-Oxley Act (SOX 404) has always played a decisive role in the quality and transparency of the financial modules of the Enterprise Resource Planning (ERP) of multinational corporations, due to the mandatory nature of the Securities and Exchange Commission (SEC). The purpose of this research is to design a set of intelligent ERP financial modules for multinational corporations that could comply with SOX 404, strengthen internal controls, and improve the security and convenience of the financial module. Thereby achieving intelligent management of financial modules and intelligent prevention of risks. Our solution could fortify the preventive controls over inappropriate payments to products with quality issues, reduce manual efforts for credit memo issuance and receipt. It could also fortify the preventive controls over financial risks potentially threatened by a sudden contingent liability concerning E&O issues. It could prevent potential and unexpected frauds arisen by a malicious user to meet the requirements of SOX over preventive and detective controls over the financial system. It could also ensure the consistency of journals between the preparer’s entry and review’s approval as well as improve the completeness and timeliness of financial statement assertions.
Art Education And Its Impact Effects On Modern Students

The paper reveals topical issues of modern art education in Russia; outlines some features of the formal (mandatory) and non-formal (optional) teaching art of modern students; the authors address to the problem of “lifelong art education”. The authors pay attention to the outcomes of the modern students’ art education; make an attempt to find out how important art remains for a modern maturing person after completing a compulsory course at general education school; give examples of the least and most effective innovative and traditional educational technologies, methods and techniques used by teachers of music and visual arts. The authors draw conclusions regarding the interests and preferences of students in the musical and visual arts field, types of diverse artistic, creative, theoretical, and musical practice; show the results of art education impact on students; reveal some pedagogical opportunities that predetermine the success and effectiveness of continuous artistic education of children and youth (a variety of used lessons forms, as well as methods, techniques of teaching art, a positive emotional background of learning, productive creative interaction and cooperation of teachers and students, emotional and semantic drama of art classes, a flexible combination of communication different styles between the teacher and students, etc.).
Generalizing Functions in Sparse Domains

We propose that when humans learn sets of relationships they are able to learn the abstract structure or type of a family of relationships, and exploit that knowledge to improve their ability to learn and generalize in the future, especially in the face of sparse or ambiguous data. In two experiments we found that participants choose patterns and extrapolate in ways consistent with sets of previously learned relations, as measured by extrapolation judgments and forced-choice tasks. We take these results to suggest that humans can detect shared abstract relations and apply this learned regularity to perform rapid and flexible generalization.
Using a Recurrent Kernel Learning Machine for Small-Sample Image Classification

Many machine learning algorithms, like Convolutional Neural Networks (CNNs), have excelled in image processing tasks; however, they have many practical limitations. For one, these systems require large datasets that accurately represent the sample distribution in order to optimize performance. Secondly, they have difficulty transferring previously learned knowledge when evaluating data from slightly different sample distributions. To overcome these drawbacks, we propose a recurrent kernel-based approach for image processing using the Kernel Adaptive Autoregressive Moving Average algorithm (KAARMA). KAARMA minimizes the amount of training data required by using the Reproducing Kernel Hilbert Space to build inference into the system. The recurrent nature of KAARMA additionally allows the system to better learn the spatial correlations in the images through one-shot or near one-shot learning. We demonstrate KAARMA's superiority for small-sample image classification using the JAFFE Face Dataset and the UCI hand written digit dataset.
An End-to-End Location and Regression Tracker with Attention-based Fused Features

Visual object tracking, which continuously generating bounding box of a specific target initialized in the first frame in a video, is one of the most fundamental and challenging tasks in computer vision. In this paper, we derive a tracker that combines correlation filter and siamese network, which both achieve superior performance and complement each other, jointly improving the tracker's performance. However, this tracker still has some problems common to most state-of-the-art trackers, that is, discrimination of features is not strong enough, and the scale estimation method is limited. To solve these problems, we propose LR-AFNet, an end-to-end tracker for target location (via the siamese network) and bounding box regression, to better locate and estimate scales, and using attention-based multi features fusion method to extract more discriminative features. Firstly, the feature extraction backbone network is redesigned to improve the location accuracy. Secondly, in order to obtain more effective features, deep and shallow features are fused with an attention mechanism, which adaptively learn the fusion weights. Finally, the bounding box regression branch is added to the siamese similarity network, forming an end-to-end trained framework. Experiments proved that our proposed method achieves competitive performance on several benchmark datasets, compared with the state-of-the-art.
Optimal Markovian Dynamic Control of Interference-Prone Server Farms

Interference is a key performance challenge faced by cloud users, and can significantly degrade application performance on virtual machines (VMs). For load-balanced cloud applications, a key question is how to distribute the load among VMs in the presence of interference. Using a Markov decision process (MDP) model, we investigate dynamic control polices to assign jobs among a cluster of VMs that are prone to interference in a system with a central queue and an arbitrary number of VMs. We characterize the structural properties of the MDP optimality equation, and we prove that the optimal control policy is a threshold policy based on the queue length. The optimal policy is characterized by multiple thresholds depending on the current conditions of the VMs, including the number of busy under-interference VMs. We discuss the existence of an ordering among such thresholds, and we prove the ordering for a two-VM system. Our numerical results show that the optimal dynamic policy can significantly improve performance compared to the the commonly employed non-idling policy. For low utilization systems, we observe improvements on the order of around 20%. We further implement the optimal policy in a real-world testbed using the HAProxy load balancer, and show that it can reduce web server response times by as much as 40%–60%, even for time-varying request rates.
Digital adult literacy in virtual learning environments

Humanity is currently experiencing constant political, economic, cultural, and social changes, produced by the interaction and use of technology. Adults are adapting to new technologies with certain obstacles and difficulties in their professional context and especially in the area of their learning. Digital literacy for adults allows them to include themselves and take advantage of technology in their daily life. Evaluation instruments have been made to measure the level of digital literacy; however, a study on the impact of the level of digital literacy of adults in the context of learning virtual environments is required, specifically the xMOOC. Therefore, this research focuses on analyzing the digital skills, that affect the completion of xMOOC courses. In addition to setting a reference framework or digital literacy assessment scale to know the skills of the participant in those courses.
Improving Quantum Noise Suppression Using Spatial Beam Profile Optimization

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text S. L. Cuozzo, A. T. Kalasky, N. Prajapati, E. Siddiqui, S. Sharifi, L. Cohen, Y. Banadaki, J. P. Dowling, I. Novikova, and E. E. Mikhailov, "Improving Quantum Noise Suppression Using Spatial Beam Profile Optimization," in Frontiers in Optics + Laser Science APS/DLS, OSA Technical Digest (Optica Publishing Group, 2019), paper JTu4A.3. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Accelerometer based quantitative outcome measurement for hand function test

Hand Function test gives relationship between functional limitations in activities and performance components such as range of motion and strength. This paper proposes a hand function test which incorporates sensor on the object. The quantitative parameters such as time and acceleration with which task is completed are measured. Accelerometer data is sensitive to the movement of the body and gives variety of movement patterns during task performance. Light dependent resistor (LDR) provides important movement queues, such as initialisation and completion of the selected activity. As sensors are embedded on the object, there is a scope for free movement of upper extremity during performance of test. Auto-time calculation during test session certainly reduces the clinician intervention time. Clinician can give more emphasis on functional movement of hand rather than spending time in documentation. The fields of application of this tool are in physiotherapy and occupational therapy.
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning

In the multi-objective reinforcement learning (MORL) paradigm, the relative importance of each environment objective is often unknown prior to training, so agents must learn to specialize their behavior to optimize different combinations of environment objectives that are specified post-training. These are typically linear combinations, so the agent is effectively parameterized by a weight vector that describes how to balance competing environment objectives. However, many real world behaviors require non-linear combinations of objectives. Additionally, the conversion between desired behavior and weightings is often unclear. In this work, we explore the use of a language based on propositional logic with quantitative semantics--in place of weight vectors--for specifying non-linear behaviors in an interpretable way. We use a recurrent encoder to encode logical combinations of objectives, and train a MORL agent to generalize over these encodings. We test our agent in several environments with various objectives and show that our agent can generalize to many never-before-seen specifications with performance comparable to single policy baseline agents. We also demonstrate our agent's ability to generate meaningful policies when presented with novel specifications and quickly specialize to novel specifications.
Studying the basics of modeling with the constructor Cuboro

The article discusses the possibility of using the Cuboro constructor in the process of forming ideas about models and modeling in primary school. The main stages of working with the constructor in the classroom or on elective classes are described.
Identifying the Barriers and Drivers of IoT Deployment in Healthcare

Healthcare is an information-intensive sector and has always benefitted from novel ICT technologies. Utilizing these technologies provides numerous benefits to the stakeholders of healthcare sector such as alternative ways to manage patient's health records, improving the accessibility and affordability of healthcare services, minimizing the errors and making the healthcare providers more accountable. The patients benefit from round-the-clock monitoring of their health which leads to better satisfaction derived from these services. Even though the IoT based healthcare offers numerous advantages, the hospital administrations generally find it hard to deploy these services. A review of the extant literature reveals that there is a scarcity of relevant literature on the barriers and drivers of IoT deployment in healthcare. This paper identifies the key barriers and drivers that are relevant to IoT deployment in healthcare.
A Comparison of Four Open Source Multi-Layer Perceptrons for Neural Network Neophytes

Neural networks and specifically Mutli-Layer Perceptrons can be used to classify data into categories or groups. The Multi-Layer Perceptron learns by processing training data. After training, the Multi-Layer Perceptron can make predictions about new data which it hadn’t seen before. There has been extensive research about Multi-Layer Perceptrons but the research is often aimed at people creating and researching neural networks. This paper is aimed at people who want to just use an open source Multi-Layer Perceptron for a project and have no real understanding or interest on how they work. The paper compares four open source Multi-Layer Perceptrons. Three different datasets are processed using similar configurations. The results are presented and conclusions are provided.
An Exploratory Study of the Use of Senses, Syntax and Cross-Linguistic Information for Subjectivity Detection in Spanish

This work presents an exploratory study of Subjectivity Detection for Spanish This study aims to evaluate the use of dependency relations, word senses and cross-linguistic information in Subjectivity Detection task. The first steps of this method include the labeling process of a Spanish corpus and a Word Sense Disambiguation algorithm. Then cross-linguistic English-Spanish information is obtained from Semcorcorpus and used together with the Spanish data. Finally, this approach (using all gathered information and supervised algorithms) was tested showing better results than the baseline method in general.
Homemade Electric Wire Electric Cable Breakpoint Nondestructive Testing Tools Based on X-Rays

Internal conductors open wire and cable is a common fault in electrical maintenance work. It is a thorny problem for maintenance. Maintenance generally has only multi-meters simple testing equipment, with little damage to the cable insulation condition often cannot judge open-circuit conductor points of a basin. Destroy the insulation not only lose wire and cable applications may also give your seat belt. As the city’s rapid economic development and urbanization advancement speeding up unceasingly, the use of the power cable has become a trend. And to ensure the safe and stable operation of the cable, reduce unplanned shutdown, naturally become one of the key research topics. We carried on the thorough research of X-ray nondestructive testing technology, can ensure that the staff under the premise of safety guarantee, in the field of cable force damage, etc. all kinds of defects of internal damage to cable is convenient, efficient, judgment, provide a reliable and effective basis for further diagnostic work.
Pseudorandom Black Swans: Cache Attacks on CTR_DRBG.

Modern cryptography requires the ability to securely generate pseudorandom numbers. However, despite decades of work on side-channel attacks, there is little discussion of their application to pseudorandom number generators (PRGs). In this work we set out to address this gap, empirically evaluating the side-channel resistance of common PRG implementations.We find that hard-learned lessons about side-channel leakage from encryption primitives have not been applied to PRGs, at all abstraction levels. At the design level, the NIST-recommended CTR_DRBG does not have forward security if an attacker is able to compromise the state (e.g., via a side-channel). At the primitive level, popular implementations of CTR_DRBG such as OpenSSL’s FIPS module and NetBSD’s kernel use leaky T-table AES as their underlying cipher, enabling cache side-channel attacks. Finally, we find that many implementations make parameter choices that enable an attacker to fully exploit side-channels and recover secret keys from TLS connections.We empirically demonstrate our attack in two scenarios. First, we carry out a cache attack that recovers the private state from vulnerable CTR_DRBG implementations when the TLS client connects to an attacker-controlled server. We then subsequently use the recovered state to compute the client’s long-term authentication keys, thereby allowing the attacker to impersonate the client. In the second scenario, we show that an attacker can exploit the high temporal resolution provided by Intel SGX to carry out a blind attack to recover CTR_DRBG’s state within three AES encryptions, without viewing output, and thus decrypt passively collected TLS connections from the victim.
VR minecraft for art

We have created a VR art museum within Minecraft and added features to make it more interactive and engaging. A scavenger hunt was added, and a player can build a sculpture after completing the scavenger hunt. The user is then provided with different colored blocks to create a painting of their own.
Error Correction Coding of Stochastic Numbers Using BER Measurement

In electric circuits, errors are ineluctable. When upper bits of binary signals flip due to noise, the value will increase or decrease drastically. On the other hand, if stochastic numbers are used, the change on their values are the same since all the bits have the same weight. Therefore, stochastic computing, a computation method based on stochastic numbers, is attracting interest. Stochastic computing does have error tolerance, but cannot restore the bit stream if the bits are erroneous. Here, this paper focuses on evaluating the error-free value from the bit error rate and the erroneous value. In this paper, we propose a method to correct errors of stochastic numbers by measuring the bit error rate and filtering the values properly. From experimental evaluations, in environment with errors of more than 21%, this proposal will give a better peak-signal-to-noise ratio compared with a conventional error correction coding.