Reviewerly/exHarmony huggingface dataset:
Feature-Aware Attentive Variational Auto-Encoder for Top-N Recommendation

Personalized recommendation has become increasingly pervasive due to its great commercial value in business. Deep neural networks can automatically exvacate the behavior patterns from the historical interaction records, which has achieved excellent results in related tasks. Among them, the variational auto-encoders have been shown to be superior for learning to rank and recommendation on massive data. However, prior work neglects the association between user behavior and side information, which affects the quality of recommendation services to some extent. In this paper, we propose a feature-aware attentive variational auto-encoder for top-N recommendation. The attention mechanism is utilized to capture the relationship between user's representation and side information through a sub network, balancing the fusion weight of attributes in the main network. In addition, this method tries to construct combination of features in the high-dimensional embedding space, helping mining the promotion of side information at a finer scale. Experiments conducted on real-world datasets demonstrate the effectiveness over the state-of-art methods.
Job Scheduling in Cloud Using Seagull Optimization Algorithm

In recent years, cloud computing technology has gained a great deal of interest from both academia and industry. Cloud computing's success benefited from its ability to offer global IT services such as core infrastructure, platforms, and applications to cloud customers around the web. It also promises on-demand offerings and new ways of pricing packages. However, cloud job scheduling is still NP-complete and has become more difficult due to certain factors such as resource dynamics and on-demand customer application requirements. To fill this void, this chapter presents the seagull optimization algorithm (SOA) for scheduling work in the cloud world. The efficiency of the SOA approach is compared to that of state-of-the-art job scheduling algorithms by having them all implemented in the CloudSim toolkit.
UX trek

User Experience (UX) is a holistic attribute focused on the quality of user-product interaction. However, the technology evolution in different contexts has not imposed limits on user-product interactions. For instance, in immersive technologies, the goal is to create a sense of immersion for the user, to the point that the boundaries between the physical and virtual worlds are blurred. There are entertainment applications for large events in that context, whose goal is to transform the audience's role from a mere spectator to an active element of the show. Thus, one of UX's most important dimensions in this context is user engagement (UE). In this work, we propose two metrics to investigate engagement through the automated analysis of logs, which are records that contain the data of the users' immersive interaction. We carried out two studies with two applications that actively engage the user in different experiences. The results showed the feasibility of using the metrics in log files, enabling both the automatic evaluation of UX and engagement understanding.
Machine Learning Techniques and Frameworks for Heterogeneous Data Fusion in Big Data Analytics

Big Data systems are now facing many challenges. Variety of data or heterogeneous nature of the data is one of the main challenges associated with Big Data. It is observed that, the data fusion can efficiently deal with this challenge. Data fusion aims at optimizing and combining the data from different sources so that the data can provide more value for decision making. Many applications can gain from data fusion. As big data applications are on rise, data fusion should become an integral part of the big data application life cycle. In this paper a review of the different data fusion techniques and their applications is provided. The main focus is to review the machine learning techniques that are applied in data fusion. This paper also covers the various frameworks for implementing machine learning. Towards the end of the paper, the recent trends in data fusion are also discussed.
Stencil Marker: Designing Partially Transparent Markers for Stacking Augmented Reality Objects

We propose a transparent colored AR marker that allows 3D objects to be stacked in space. Conventional AR markers make it difficult to display multiple objects in the same position in space, or to manipulate the order or rotation of objects. The proposed transparent colored markers are designed to detect the order and rotation direction of each marker in the stack from the observed image, based on mathematical constraints. We describe these constraints to design markers, the implementation to detect its stacking order and rotation of each marker, and a proof-of-concept application Totem Poles. We also discuss the limitations of the current prototype and possible research directions.
Managing Service Level for Academic Information System Help Desk for XYZ University Based on ITIL V3 Framework

Ministry of Research, Technology, and Higher Education of the Republic of Indonesia stated that every university in Indonesia needs to implement information technology governance, one of which is information technology service governance. XYZ University as one of the universities in Indonesia needs to implement the governance of information technology services. The implementation of information technology service governance can be using the Information Technology Infrastructure Library Version 3 (ITIL V3) framework. One of the information system services that often have issues with help desk services is academic information systems. The issue of academic information system help desk service issues is resolved by using one of the processes in ITIL V3, namely service level management. In this observation, a service level management process was carried out to produce three documents, namely the Service Level Requirement (SLR), Service Level Agreement (SLA), and Operational Level Agreement (OLA). This research includes three main processes, namely collecting data and information, creating documents, and verification and validation. The method of collecting data and information is carried out using interview techniques, the process of making documents, validation, and verification is carried out by conducting group discussion forums with service providers and service users. In this case, service users are the academics of XYZ University, while service users are the Information and Communication Technology Department of XYZ University. This observation resulted in three main documents in service level management, namely SLR, SLA, and OLA, for academic information system help desk services.
Hamming distances of constacyclic codes of length 3p and optimal codes with respect to the Griesmer and Singleton bounds

Let p≠3 be a prime, s, m be positive integers, and λ be a nonzero element of the finite field Fpm. In [22] and [20], when the generator polynomials have one or two different irreducible factors, the Hamming distances of λ-constacyclic codes of length 3ps over Fpm have been considered. In this paper, we obtain that the Hamming distances of the repeated-root λ-constacyclic codes of length lps can be determined by the Hamming distances of the simple-root γ-constacyclic codes of length l, where l is a positive integer and λ=γps. Based on this result, the Hamming distances of the repeated-root λ-constacyclic codes of length 3ps are given when the generator polynomials have three different irreducible factors. Hence, the Hamming distances of all such constacyclic codes are determined. As an application, we obtain all optimal λ-constacyclic codes of length 3ps with respect to the Griesmer bound and the Singleton bound. Among others, several examples show that some of our codes have the best known parameters with respect to the code tables in [19].
Suppleness of Multi-Tenancy in Cloud Computing: Advantages, Privacy Issues and Risk Factors

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Weak Speech Supervision: A case study of Dysarthria Severity Classification

Machine Learning methodologies are making a remarkable contribution, and yielding state-of-the-art results in different speech domains. With this exceptionally significant achievement, a large amount of labeled data is the largest bottleneck in the deployment of these speech systems. To generate massive data, hand-labeling training data is an intensively laborious task. This is problematic for clinical applications where obtaining such data labeled by speech pathologists is expensive and time-consuming. To overcome these problems, we introduce a new paradigm called Weak Speech Supervision (WSS), a first-of-its-kind system that helps users to train state-of-the-art classification models without hand-labeling training data. Users can write labeling functions (i.e., weak rules) to generate weak data from the unlabeled training set. In this paper, we provide the efficiency of this methodology via showing the case study of the severity-based binary classification of dysarthric speech. In WSS, we train a classifier on trusted data (labeled with 100% accuracy) via utilizing the weak data (labeled using weak supervision) to make our classifier model more efficient. Analysis of the proposed methodology is performed on Universal Access (UA) corpus. We got on an average 35.68% and 43.83% relative improvement in terms of accuracy and F1-score w.r.t. baselines, respectively.
An artificial neural network as a predictor of electrical characteristics of nanoelectronic device channel based on a low-dimensional heterostructure

Abstract In this paper a computational algorithm for calculating current density of low-dimensional semiconductor heterostructures based on an artificial neural network is proposed. The neural network training is performed using the quantum-mechanical model of Green’s Functions.
Forming Higher School Students’ Inclusive Competence: Efficiency of Organizational Teaching Models

The paper analyses efficiency of organizational teaching models aimed at forming higher school students’ inclusive competence. The authors identify prerequisites for origin of the “inclusive competence” notion, reveal its essence, describe methodological approaches to forming students’ inclusive competence, provide a comparative survey of the existing models of students’ inclusive competence formation in the process of teaching pedagogical disciplines. Scientific originality of the research involves justifying the thesis that efficiency of the organizational teaching models under study depends on the level of students’ interest in the discipline, level of their involvement in problematic of social and educational inclusion, in choice of a learning trajectory, forms and methods of teaching. The research findings are as follows: the notion “inclusive competence” is defined; assessment criteria for inclusive competence are ascertained; conclusion is made that the elective education model is more efficient in terms of students’ inclusive competence formation.
Spoken Language Identification of Four Tibeto-Burman languages

Bodo, Dimasa, Rabha and Tiwa are languages of the Tibeto-Burman language family. These languages are spoken in north-east India and surrounding areas. Bodo is also one of the 22 official languages of the Government of India. Consequently, spoken language systems had been developed for Bodo. In contrast, similar systems for the other languages are yet to be developed. Here, we present the details of an automatic Language Identification (LID) system that identifies the language of an input speech file without using phonetic information. The text-independent LID system was implemented using Gaussian mixture model with Mel-Frequency Cepstral Coefficients (MFCCs) as features. A 3-fold cross validation methodology was adopted to assess the performance of the system. The accuracy of the LID system was the highest when suprasegmental features were used in addition to segmental features. The best LID system, using a 62-dimensional feature vector consisting of 13 MFCCs and 49 shifted delta coefficients, yields 92.7% accuracy when the duration of the test data is 3 seconds.
A Study of Hyper-Parameter Tuning in The Field of Software Analytics

Software defect is defined as the divergence from the expected or anticipated behavior of the software. Humans can manually detect the defects but this process is always time-consuming and tiresome. To save human effort, machine learning algorithms are used to predict faulty modules in our software. One of the major aspects which are overlooked is the performance measure of these algorithms. Defect Predictors can be optimized by tuning the parameters of the algorithm. The defect predictors are easy to use. The paper attempts to analyze and compare various methodologies to tune the defect predictors. The research papers which are analyzed here have used data-set from the PROMISE repository, open-source JAVA systems, SEACRAFT repository to train and test our methodologies. It is concluded that tuning the defect predictor is not a complicated task, and it enhanced the performance of the defect predictor to a great extent. Thus it becomes imperative to tune the defect predictors using optimization algorithms before using them for detecting anomalies in our software.
More than Meets the Eye: Technical Services Work as Outreach

Outreach in libraries has traditionally been considered the realm of public services, where librarians interact one-on-one with our patrons at the reference desk, run social media accounts, and other activities of a similar vein. In today's evolving library world, it is time to challenge outreach in its traditional sense and consider technical services work and its associated duties as outreach. This article delves into technical services work (including cataloging, archival processing, shelf maintenance, etc.) and how the results (bibliographic records, metadata, authority control etc.) are in themselves outreach.
Lightweight Color Image Demosaicking with Multi-Core Feature Extraction

Convolutional neural network (CNN)-based color image demosaicking methods have achieved great success recently. However, in many applications where the computation resource is highly limited, it is not practical to deploy large-scale networks. This paper proposes a lightweight CNN for color image demosaicking. Firstly, to effectively extract shallow features, a multi-core feature extraction module, which takes the Bayer sampling positions into consideration, is proposed. Secondly, by taking advantage of inter-channel correlation, an attention-aware fusion module is presented to efficiently reconstruct the full color image. Moreover, a feature enhancement module, which contains several cascading attention-aware enhancement blocks, is designed to further refine the initial reconstructed image. To demonstrate the effectiveness of the proposed network, several state-of-the-art demosaicking methods are compared. Experimental results show that with the smallest number of parameters, the proposed network outperforms the other compared methods in terms of both objective and subjective qualities.
Simulation of football sport PID controller based on BP neural network

Multi-agent reinforcement learning in football simulation can be extended by single-agent reinforcement learning. However, compared with single agents, the learning space of multi-agents will increase dramatically with the increase in the number of agents, so the learning difficulty will also increase. Based on BP neural network as the model structure foundation, this research combines PID controller to control the process of model operation. In order to improve the calculation accuracy to improve the control effect, the prediction output is obtained through the prediction model instead of the actual measured value. In addition, with the football robot as the object, this research studies the multi-agent reinforcement learning problem and its application in the football robot. The content includes single-agent reinforcement learning, multi-agent system reinforcement learning, and ball hunting, role assignment, and action selection in football robot decision strategies based on this. The simulation results show that the method proposed in this paper has certain effects.
LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs

This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of low-resolution ADCs could largely reduce hardware complexity and circuit power consumption, however, it makes the channel state information almost blind to the BS, hence causing difficulty in solving the equalization problem. In this paper, we consider a supervised learning architecture, where the goal is to learn a representative function that can predict the targets (constellation points) from the inputs (outputs of the low-resolution ADCs) based on the labeled training data (pilot signals). Especially, our main contributions are two-fold: 1) First, we design a new activation function, whose outputs are close to the constellation points when the parameters are finally optimized, to help us fully exploit the stochastic gradient descent method for the discrete optimization problem. 2) Second, an unsupervised loss is designed and then added to the optimization objective, aiming to enhance the representation ability (so-called generalization). Lastly, various experimental results confirm the superiority of the proposed equalizer over some existing ones, particularly when the statistics of the channel state information are unclear.
MULTISEM at SemEval-2020 Task 3: Fine-tuning BERT for Lexical Meaning

We present the MULTISEM systems submitted to SemEval 2020 Task 3: Graded Word Similarity in Context (GWSC).We experiment with injecting semantic knowledge into pre-trained BERT models through fine-tuning on lexical semantic tasks related to GWSC.We use existing semantically annotated datasets and propose to approximate similarity through automatically generated lexical substitutes in context.We participate in both GWSC subtasks and address two languages, English and Finnish.Our best English models occupy the third and fourth positions in the ranking for the two subtasks.Performance is lower for the Finnish models which are mid-ranked in the respective subtasks, highlighting the important role of data availability for fine-tuning.
Aerospace target detection based on complex background

Problems such as small object and helicopter (and ship) mutual occlusion are difficult to detect in complex backgrounds, which poses great challenges to the accuracy and real-time detection of helicopter and ship. The application of the YOLOV3 algorithm with high real-time performance to the detection of helicopter and ship under complex backgrounds cannot reach a satisfactory level. This paper has made four improvements on the basis of YOLOV3 algorithm: l)Replace the downsampling in the backbone network with dilated convolution, maintain a higher resolution and a larger receptive field, improve the accuracy of the model for small object detection; 2)Introduce channel attention module to extract more semantic information of target objects; 3)optimizes the non-maximum suppression algorithm by linear declining the confidence score to improve the model's ability to detect occlusion helicopter(and ship); 4) IOU algorithm is optimized and solved When IOU is used as Loss, Loss =0 due to the disjointness between the prediction box and the groundtruth, which makes it impossible to optimize. The results show that the improved YOLOV3 can detect small object and occlude helicopter(and ship), and improve the detection accuracy from 74.04% to 89.04% under the premise of ensuring real-time performance.
Design and Validation of Two-Stage Motion Control System for By-Air Quantum Key Distribution

In this paper, we present a dynamic visual servo control for receiver and transmitter positioning in a by-air quantum key distribution network using a robot manipulator. We discuss the positioning system with pre-alignment and fine-positioning stages. At the pre-alignment stage, the use of image-based visual servoing and dynamic controllers with payload variation is considered. We consider the use of two classical controllers (PD+ gravity and computed-torque controller) and an additional simple time-delay controller. As a fine-positioning stage, we describe approaches that can be used for precise adjustment of a transmitter. We conclude the paper with simulation results using a model of 5-dof robot manipulator KUKA youBot.
How to derive allomorphy: a case study from Czech

Abstract The paper discusses a three-way allomorphic pattern of neuter-gender stems in Czech. We argue that there are two surface alternations involved in the three-stem pattern, i.e. a-e and t -zero, and that the two alternations are driven by two distinct mechanisms, i.e. suppletion and regular phonological computation respectively. We postulate two suppletive stem-building suffixes that are lexically connected via the Nanosyntactic tool of pointers. We argue that the default a -initial suffix spells out the neuter gender and its suppletive e -initial variant is a portmanteau morpheme of the (neuter) gender and (singular) number. We further propose that the suppletive suffix is stored with a floating consonant that does not surface word-finally due to regular phonological computation.
TTCN-3 based NG112 Test System and Playground for Emergency Communication

Great research efforts are made towards transferring additional data, such as location and sensor information, within the duration of an NG112 call. Especially in the field of emergency communication, the transmission of eHealth sensor data, which provide information about the vital parameters of a person in need of help, could improve the overall rescue operation. Since emergency calls are time-critical, it is necessary to analyse the impact of the attached sensor data on the emergency call system. In this paper, we present a TTCN-3 Test System that emulates smart devices by sending simulated sensor data via Bluetooth to a smartphone to trigger automated emergency calls. This emergency call is routed through an NG112 platform back to the Test System to measure the latency between sending the data and receiving the call. By incrementing the number of simulated sensors, the impact of the attached sensor data can be evaluated.
A research on an intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties

The intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties—the “Shang Zhou Bronze Inscriptions Intelligent Mirror”—was successfully invented in Shanghai. This mirror, based on the computer technology of artificial intelligence (AI) image recognition and image retrieval, succeeds in automagical recognition of bronze inscriptions, both single letters and full texts. This research leads the trend of the AI recognition of Ancient Chinese characters and accumulates valuable experience for the development of inter-disciplinary research on Chinese character recognition. This essay emphasizes the importance of the bronze inscriptions of the Shang and Zhou dynasty database in the AI recognition of bronze inscriptions, introduces the functional components of this tool, and shares the whole research process in order to offer experience for the related research on AI recognition of other types of Ancient Chinese characters as well as ideographs in the world scope. “Shang Zhou Bronze Inscriptions Intelligent Mirror” as a tool for bronze inscription recognition also has room for improvement and support, and guidance from experts in similar areas is greatly welcomed.