Reviewerly/exHarmony huggingface dataset:
Learning to Count Words in Fluent Speech enables Online Speech  Recognition

Sequence to Sequence models, in particular the Transformer, achieve state of the art results in Automatic Speech Recognition. Practical usage is however limited to cases where full utterance latency is acceptable. In this work we introduce Taris, a Transformer-based online speech recognition system aided by an auxiliary task of incremental word counting. We use the cumulative word sum to dynamically segment speech and enable its eager decoding into words. Experiments performed on the LRS2, LibriSpeech, and Aishell-1 datasets of English and Mandarin speech show that the online system performs comparable with the offline one when having a dynamic algorithmic delay of 5 segments. Furthermore, we show that the estimated segment length distribution resembles the word length distribution obtained with forced alignment, although our system does not require an exact segment-to-word equivalence. Taris introduces a negligible overhead compared to a standard Transformer, while the local relationship modelling between inputs and outputs grants invariance to sequence length by design.
Structure of Multiple Mirror System from Kaleidoscopic Projections of  Single 3D Point

This paper proposes a novel algorithm of discovering the structure of a kaleidoscopic imaging system that consists of multiple planar mirrors and a camera. The kaleidoscopic imaging system can be recognized as the virtual multi-camera system and has strong advantages in that the virtual cameras are strictly synchronized and have the same intrinsic parameters. In this paper, we focus on the extrinsic calibration of the virtual multi-camera system. The problems to be solved in this paper are two-fold. The first problem is to identify to which mirror chamber each of the 2D projections of mirrored 3D points belongs. The second problem is to estimate all mirror parameters, i.e., normals, and distances of the mirrors. The key contribution of this paper is to propose novel algorithms for these problems using a single 3D point of unknown geometry by utilizing a kaleidoscopic projection constraint, which is an epipolar constraint on mirror reflections. We demonstrate the performance of the proposed algorithm of chamber assignment and estimation of mirror parameters with qualitative and quantitative evaluations using synthesized and real data.
How Question Characteristics Impact Answer Outcome on Social Question-and-Answer Websites

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.
Saving and Restoring World Mapping Data to Create a Persistence AR Experience

Starting from iOS 12, ARKit has the capability to persist in world mapping data. In the past, you can’t save the AR world mapping data. iOS 12 has given developers the power to create a persistent AR experience. If you are interested in learning about building apps that persist world mapping data in augmented reality, this tutorial is written for you.
An Emoji Is Worth a Thousand Words

Face emojis are regularly used in reviews on online shopping sites to add richness and/or levity to the text. However, few researchers have investigated the use of face emojis in consumer reviews. This paper examined the effects of the number of face emojis (none, one and three) in user-generated reviews (UGRs) on trustworthiness and purchase intention. We found that the number of face emojis in UGRs has a profound impact on consumers. A greater number of face emojis correlates with a greater degree of review trustworthiness. However, the effect of emojis on purchase intention changes depending on the situation. Additionally, when there is a profile picture, consumers pay more attention to it, and the positive effect due to face emojis disappears. Moreover, the gift giver-recipient relationship moderates the effect of face emojis on user decisions. The findings of our work have theoretical and managerial implications with respect to providing a new means of understanding consumer perceptions of products, from which business can benefit by improving sales and policies.
SERVICE-ORIENTED INTEGRATION ARCHITECTURE FIFTEEN YEARS LATER

In computer science, systems are typically divided into two categories: software and hardware. However, there is an additional layer in between, referred to as middleware, which is a software "pipeline," an operation, a process, or an application between the operating system and the end user. This article aims to define middleware and reflect on its necessity, as well as address controversies about when and where it applies. It also explores the application of middleware in emerging technologies such as cloud computing and the IoT (Internet of Things), as well as future middleware developments.
Leverage Computer Vision for Cost-Effective Learning Paradigm

In today’s world, technologies like virtual and augmented reality grow every second and provide diverse dimensions to various other domains. These technologies are more likely to bring users into the virtual world to help them understand multiple concepts more closely. In fact, in the present era, little kids of the current generation are more curious about the know-how of things in a more visual way. The curiosity among the young kids to understand various concepts requires a better learning environment. The kids may explore the working of the objects in 3D by visualizing the natural movements. But practically, this demands a virtualized environment for them to understand these things and that too in a safe environment. Now, this is somehow getting possible due to the latest advancement within these technologies which opens space for exploration for various domains. Recently Google has developed ARCore, an SDK that allows the development of augmented reality-based applications. In this virtualized environment, the user can immerse themselves even when they are not physically present. In our model, we have facilitated the user to explore different dimensions of the computer vision which are required for understating various concepts in a very cost-effective manner. In our model, we have provided a vision-based interface to the user that replaces peripheral hardware without affecting their performances and facilitates them to use these interfaces embedded with AR to better understand 3D concepts.
Smart Child Monitoring System

The number of working moms has risen dramatically in recent years. As a result, for many families, child care has become challenging. As a result, most parents send their children to their grandparents' homes or to baby-sitting services. However, in both regular and extraordinary situations, parents are unable to continually check their children's whereabouts and activities. As a result, a Smart Infant Monitoring System is proposed as a cost-effective and efficient IoT-based system for real-time monitoring.
Meningkatkan Keterampilan Literasi Siswa Melalui Pemahaman Literasi Digital pada Masa Pandemi Covid-19

The Covid-19 pandemic has had an impact on various sectors, one of which is the education sector. The learning system that was originally implemented face-to-face in schools is now being replaced with an online learning system. This is also one of the parents' complaints. One of them is in Petung Village, Bangsalsari District, Jember Regency, where children are less than optimal when studying at home. In addition, parents who do not understand digital technology find it difficult to provide assistance. KKN BTV 3 UNEJ students are here with the aim of providing an understanding of Digital Literacy to parents and children in learning at home. The main focus in this KKN activity is to provide understanding about Digital Literacy to parents and students. The implementation of the program is carried out in 4 stages, namely, Preparation, Socialization, Learning Education, Training, and Evaluation. The results achieved in this activity are an understanding of Digital Literacy for parents and can improve students' literacy skills, as well as effectiveness in learning activities at home.
Topology in Photonic Discrete-Time Quantum Walks: A Comprehensive Review

We present a comprehensive review of photonic implementations of discrete-time quantum walks (DTQW) in the spatial and temporal domains. Moreover, we introduce a novel scheme for DTQWs using transverse spatial modes of single photons and programmable spatial light modulators (SLM) to manipulate them. We discuss current applications of such photonic DTQW architectures in quantum simulation of topological effects in photonic systems.
Strings

This chapter focuses on some of the most common String methods and techniques for working with String objects. In fact, they are one of the most used data types in any programming language. They can be used to obtain text from a keyboard, print messages to a command line, and much more. Given that strings are used so often, there have been many features added to the String object over time to make them easier to work with. After all, a string is an object in Java, so it contains methods that can manipulate the contents of the string.
A Study and Experimentation on Bitcoin Price Forecasting with Time Series Analysis and Recurrent Neural Network (RNN)

Bitcoin is a peer-to-peer payment system and a digital currency as well as a cryptocurrency. The creation and transfer of money are controlled by cryptography. It is shared through a growing list of blocks linked through a network called the blockchain. The price of bitcoin varies from one day to another, and from the literature, it has been found that there are many fluctuations in its rate. Moreover, many models have been developed to predict the price of bitcoin. This research aims to develop a time series model for solving the problem of bitcoin price prediction. Time series forecasting is a widely applied model, and it learns to predict the future from past performance of series of data. It integrates Auto-Regressive Integrated Moving Average (ARIMA) and recurrent neural network (RNN) to improve the prediction accuracy. In this model, recurrent loops in the activation function of the RNN model are added with ARIMA. The proposed model predicts the bitcoin price with higher accuracy compared with existing models.
IS TOEFL SUBJECT NECESSARY FOR ENGLISH EDUCATION STUDY PROGRAM? LECTURERS’ PERSPECTIVE

This study attempts to investigate the lecturer’s perspective, whether TOEFL subject is necessary for English education study program or not. This study is a case study approach that is naturally qualitative. The study involved three lecturers of English Education Study Program as participants.. This study was conducted at one State University on North Sumatera, Indonesia, majoring in English Education. The participants were interviewed through interview via telephone. The lecturers were asked and interviewed about their opinions about the existence and the implementation of TOEFL subjects in the study program. The data were analyzed by using percentage calculation. After the participants answered the interview, the data were then classified according to its origin. The respondents have different perspective on this study. Two respondents stated that TOEFL should not be included as a subject to be studied by the English Education students in case TOEFL is tips and trick skill based, not knowledge base. In contrary, one of the respondent stated that TOEFL is required to be added to the curriculum, in case the students have not understood all aspects in reading which then TOEFL should be learned by students in specific credit subject.
Quantum Teleportation via a Two-Qubit Heisenberg XXX Chain With X-Component of Dzyaloshinskii-Moriya Interaction

This article investigates the quantum teleportation via a two-qubit Heisenberg XXX chain with the x-component of the Dzyaloshinskii-Moriya interaction. The correspondence between the output state and the fidelity of entanglement teleportation expressions was constructed utilizing physical variables associated with the selected system. Our findings suggest that temperature, the spin coupling constant $J$, and the x-components Dx may all contribute to the degree of intricacy between states and, therefore, to the possibilities of teleportation protocols. Additionally, these results suggest that the states' separability needs either a high-temperature regime, strong spin-orbit coupling through the Dzyaloshinskii-Moriya interaction, or a ferromagnetic chain. The system states grow increasingly entangled even with an antiferromagnetic chain, weak spin-orbit coupling, or low temperature. As a consequence, the channel becomes entangled, making the teleportation protocol conceivable and feasible.
A New Approach for Explicit Distributional Semantic Vector Representation Based on Dependency Distance and Bias Elimination

We consider the problem of the semantic representation of words based on explicit basis word vectors. We focus on explicit semantic vectors in which each basis vector is equivalent to a unique word. In this paper, we propose a new approach to represent semantic vectors explicitly. The approach consists of three steps: First, we introduce two new factors called word similarity (WS) and the number of zeroes (NZ) for choosing informative context words. Second, we propose a dependency distance method by incorporating the syntactic relations of the words in the sentences. This means that the method uses not only semantic information but also syntactic one. Third, we propose two shifting methods based on WF and WS factors to eliminate the bias of the PPMI matrix. In this work, we use the ukWaC corpus to select context words and compute co-occurrence matrices. Then, we examine the results on MEN, RG-65, SimLex-999, and WordSim353 test sets. We report the performance in comparison with a Baseline in which, the word frequency has been used. The Baseline method uses a window to fix the influence of the neighboring words. Our approach improves the Spearman correlation coefficient for MEN, RG-65, SimLex-999, and WordSim353 datasets by 3.87%, 10.25%, 5.04%, and 5.24%, respectively. So, the experiment results show that the proposed context words selection, incorporating dependency distance, and using the WF-based shifting method significantly improve the performance.
Inversion Attacks against CNN Models Based on Timing Attack

Model confidentiality attacks on convolutional neural networks (CNN) are becoming more and more common. At present, model reverse attack is an important means of model confidentiality attacks, but all of these attacks require strong attack ability, meanwhile, the success rates of these attacks are low. We study the time leakage of CNN running on the SoC (system on-chip) system and propose a reverse method based on side-channel attack. It uses the SDK tool-profiler to collect the time leakage of different networks of various CNNs. According to the linear relationship between time leakage, calculation, and memory usage parameters, we take the profiling attack to establish a mapping library of time and the different networks. After that, the smallest difference between the measured time of unknown models and the theoretical time in the mapping library is considered to be the real parameters of the unknown models. Finally, we can reverse other layers even the entire model. Based on the experiments, the reverse success rate of common convolutional layers is above 78.5%, and the reverse success rates of different CNNs (such as AlexNet, ConvNet, LeNet, etc.) are all above 67.67%. Moreover, the results show that the success rate of our method is 10% higher than the traditional methods on average. In the adversarial sample attack, the success rate reached 97%.
QUALITATIVE ANALYSIS OF AMDK AND AMIU AROUND UNIVERSITY OF MUHAMMADIYAH KUDUS

This study is intended to find the quality of bottled drinking water (AMDK) and drinking water (AMIU) sold around University of Muhammadiyah Kudus, in terms of organoleptic and microbiological parameters in drinking water according to SNI 01-3554-2006 and PMK No. 492 of 2010. The test is carried out using 10 sample of drinking water. They are 7 sample of AMDK (Ades, Airmu, Aqua, Cleo, Crystalin, Le mineral and Vit) and 3 sample of AMIU (Pasuruhan, Prambatan and Purwosari depots). The research consists of chemical physics which includes testing of TDS, pH, and three heavy metal elements which includes testing for the content of the elements Fe, Mn and Cl2 (chlorine), organoleptic and mikrobiologis (TPC, presence of E. coli and total coliform).The results of research shows that of physical and chemical measurements, the total dissolved solid (TDS) results 0 for Airmu and Cleo (demineral) and below 500 ppm for 8 samples of mineral water, this is according with the TDS requirements of SNI, &gt; 10 ppm for demineralized and &gt; 500 ppm for mineral water. The results of the pH test or the degree of acidity are in normal pH, which are around 7 out of 9 samples and only one with an alkaline condition is 8.37, namely the sample from the Pasuruhan depot. The test for the presence of heavy metals, Fe, Mn, is in good condition, showing the number zero with the brand brand test kit. Whereas for testing the presence of chlorine content, the results were above the threshold, namely 0.05 ppm from the Pasuruhan depot water sample, 0.02 ppm from the Prambat depot and the Purwosari depot. This is not in accordance with SNI provisions for chlorine content of 0 ppm. The organoleptik of all samples is in normal condition. Whereas, microbiologically, the TPC results before dilution were only Cleo and Ades which were suitable for consumption because they were absent from bacterial contamination. While the TPC results after dilution, testing the difference between E. Coli and Total coliform resulted in 7 samples of bottled drinking water which were safe and feasible as drinking water according to SNI 01-3553-2006 and 3 samples AMIU does not fulfil PMK No. 492/2010, as drinking water requirements.Keywords: AMDK, AMIU, Organoleptic Quality, Microbiology
Multi-camera object localization as a layer of a High-Definition map

People use conventional maps for orientation in various environments. With the increase of traffic participant number, restrictions of conventional maps result in traffic congestion and an increased number of accidents. We could overcome such restrictions of conventional maps with High Definition (HD) maps. On top of conventional maps, HD maps provide detailed real-time information about traffic participants in monitored areas. Consequently, HD maps enable faster deployment of autonomous driving systems and provide significant improvement to Smart Cities. We can provide such additional real-time information as a result of an object detection algorithm. We propose a novel solution in developing an HD map layer by combining multiple object detection results. Traffic is monitored from multiple cameras observing vehicles and pedestrians from different points of view. We tested our solution on an urban environment, upgrading the public Geolocation map to an HD map.
Featured Cover

The cover image is based on the Research Article An intelligent course keeping active disturbance rejection controller based on double deep Q-network for towing system of unpowered cylindrical drilling platform by Yuemin Zheng et al., https://doi.org/10.1002/rnc.5740.
vrfinder

Current methods to analyze the Internet's router-level topology with paths collected using traceroute assume that the source address for each router in the path is either an inbound or off-path address on each router. In this work, we show that outbound addresses are common in our Internet-wide traceroute dataset collected by CAIDA's Ark vantage points in January 2020, accounting for 1.7% - 5.8% of the addresses seen at some point before the end of a traceroute. This phenomenon can lead to mistakes in Internet topology analysis, such as inferring router ownership and identifying interdomain links. We hypothesize that the primary contributor to outbound addresses is Layer 3 Virtual Private Networks (L3VPNs), and propose vrfinder, a technique for identifying L3VPN outbound addresses in traceroute collections. We validate vrfinder against ground truth from two large research and education networks, demonstrating high precision (100.0%) and recall (82.1% - 95.3%). We also show the benefit of accounting for L3VPNs in traceroute analysis through extensions to bdrmapIT, increasing the accuracy of its router ownership inferences for L3VPN outbound addresses from 61.5% - 79.4% to 88.9% - 95.5%.
ATTENTION BASED IMAGE CAPTIONING USING DEEP LEARNING

The process of deriving written descriptions from a picture based on the items and behaviours portrayed is known as image caption generation. The resulting description will represent what is in the image and the interactions among the objects. However, like with any other image processing challenge, recreating this characteristic in an artificial system is a difficult feat, and so the adoption Deep Learning procedures can help to tackle the problem. The main goal of this project is to generate an image captioning model that can provide a concise and relatable description of the picture by incorporating an attention mechanism. Ideally, the model can be separated into two components – one is called Encoder and the other is a Decoder. The encoder used here is a Google InceptionV3 pre-trained model. And the decoder is a language-based model, Gated Recurrent Unit (GRU) to translate the characteristics and objects provided by the encoder into natural sentences. Furthermore, the model's effectiveness is aided by the attention mechanism. To conclude, the model is trained on a subset of the MS COCO dataset, which contains about 80,000 pictures with at least five descriptions each and evaluated based on the BLEU score. To reduce the loss, the model weights are tuned using the categorical cross-entropy loss function. The results of the model are promising and competitive.
Deformation of mine roadways about trucks

Roadway deformations result in elevated rolling resistances and the destruction of the road itself. There are conceptually three behavioural zones: plastic rutting around the tyre, fully elastic at a distance from the truck and a hybrid semi-elastic zone in between. It is the semi elastic zone which is of most interest here. It is particularly marked in some soft oilsands roads where a 'roll' can often be observed around vehicles. The authors have investigated the semi elastic zone using photogrammetry and tiltmeters. An attempt is made to tie observations with a simple elastic (Boussinesq) solution for a point load on a semi infinite half space.
Microseismic monitoring in salt

Sudden stress releases in rock salt, which may be due to high mechanical or thermal load, can be detected and located by a passive seismic array. The requirements to be met by such a system are explained, and some experiences with a system, which is in operation in the Asse since 1980 covering the whole mine, are described. A second array of a smaller scale is being installed about a new thermal test field. An outline of its design is given.