Reviewerly/exHarmony huggingface dataset:
MINDS: A Translator to Embed Mathematical Expressions Inside SPARQL Queries

Abstract The recent deployments of semantic web tools and the expansion of available linked datasets have given users the opportunity of building increasingly complex applications. These emerging use cases often require queries containing mathematical formulas such as euclidean distances or unit conversions. Currently, the latest SPARQL standard (version 1.1) only embeds basic math operators. Thus, to address this shortcoming, some popular SPARQL evaluators provide built-in tools to cover specific needs; however, such tools are not standard yet. To offer users a more generic solution, we propose and share MINDS, a translator of mathematical expressions into SPARQL-compliant bindings which can be understood by any evaluator. MINDS thereby facilitates the query design whenever mathematical computations are needed in a SPARQL query.
Real-Time Vehicle Detection from Short-range Aerial Image with Compressed MobileNet

Vehicle detection from short-range aerial image faces challenges including vehicle blocking, irrelevant object interference, motion blurring, color variation etc., leading to the difficulty to achieve high detection accuracy and real-time detection speed. In this paper, benefiting from the recent development in MobileNet family network engineering, we propose a compressed MobileNet which is not only internally resistant to the above listed challenges but also gains the best detection accuracy/speed tradeoff when comparing with the original MobileNet. In a nutshell, we reduce the bottleneck architecture number during the feature map downsampling stage but add more bottlenecks during the feature map plateau stage, neither extra FLOPs nor parameters are thus involved but reduced inference time and better accuracy are expected. We conduct experiment on our collected 5-k short-range aerial images, containing six vehicle categories: truck, car, bus, bicycle, motorcycle, crowded bicycles and crowded motorcycles. Our proposed compressed MobileNet achieves 110 FPS (GPU), 31 FPS (CPU) and 15 FPS (mobile phone), 1.2 times faster and 2% more accurate (mAP) than the original MobileNet.
Nuclide Spectrum Peak Searching Algorithm Based on Multiple Morphological Structuring Elements

Abstract Considering the characteristics of the traditional peak searching methods of nuclide spectrum and morphological transformation in the image processing, a nuclide spectrum peak searching method based on multiple morphological structuring elements is proposed. Four flat structural elements at an angle of 0, 45, 90, and 135 to the horizontal line are utilized, then the morphological transformation of the nuclide spectrum is performed, and the details of the different directions of the nuclide spectrum curve are better controlled. The algorithm proposed in this paper improves the ability of the morphological peak searching and the peak information is preserved well, meanwhile, the method has a better peak recognition effect.
Indonesian EFL Learners’ Perception on the Internet Use in Language Learning

This study aims at 1) scrutinizing Indonesian EFL learners’ perception on the use of internet in their learning process and 2) uncovering how long they spent their time with internet for academic and non-academic purposes. To obtain the required data, the adapted version of Shin & Son’s (2007) questionnaire was distributed to 100 Indonesian EFL college learners. The results indicate that most of the participants have positive perceptions and good responses toward the use of internet either in academic and non-academic purposes. They asserted that internet facilitated them with various types of information and stated that internet makes them easier to access varied and up to date information. However, they admitted that internet contributes to plagiarism practice among students and, due to the convenience offered by internet, it makes them addicted in internet use for information searching and in turn they pay less attention to the written resources. In relation to time they spent for internet use, most students reported that they used it very frequently for their academic purpose in which googling is their most favorable activity, but more surprisingly, they spent a greater time in going online for non-academic purposes, between 5 to 10 hours a day.
The Influence of Job Insecurity for Airline Workers due to the 4th Industrial Revolution upon Organizational Engagement and Organizational Civil Behaviors

In the world of faster-paced globalization and informationization due to the 4th Industrial Revolution, the airline service industry depends heavily on its human resources for its successful conduct of business as the entirety of its services are performed by its employees. Therefore, in this study, the issue of job insecurity for the airline workers due to the 4th Industrial Revolution and review the impact upon the organizational engagement and organizational civil behaviors of the airline crews.<BR> This study was conducted over one month, surveying a total of 362 airline crews. The data from the survey were analyzed using SPSS 23.0 and AMOS 23.0 statistics suites. The findings of the analyses were as follows; first, the job insecurity of the crews had a negative impact on organizational engagement; second, the job insecurity of the crews had a negative impact on the organizational civil behaviors; third, the organizational engagement among the crews had a positive impact on the organizational civil behaviors; and fourth the engagement had a significant mediating effect in the relationship between the personal job insecurity and the organizational civil behavior.
Online quantum state tomography of N-qubit via continuous weak measurement and compressed sensing

In this paper, we propose an online state tomography method for [Formula: see text]-qubit quantum system based on the continuous weak measurement and compressed sensing (CS). The quantum system is described by stochastic master equation. The continuous weak measurement operators for the [Formula: see text]-qubit quantum system, which are indirectly acted on the quantum system, are derived according to the measurement operator results of two-level quantum system. The online time-varying measurement operators are obtained by means of the dynamic evolution equation of the system. The quantum state is online estimated by solving the optimization problem of minimizing the two-norm with the positive definite constraints of density matrix, and we use the nonnegative least squares algorithm to solve the optimization problem. CS theory is used to reduce the number of the measurements in the process of online state estimation. In the numerical experiments, we study the effects of external control field, measurement rate and the different numbers of qubits on the performance in the proposed method. The minimum required numbers of measurements for 2, 3, 4 and 5 qubits are found. The normalized distance and fidelity of our proposed method can achieve satisfying accuracy with small number of measurements.
Engaging families in a 10-week, AI global competition

The 2019 season of Technovation Families engaged ~9200 under-resourced 3rd-8th grade students, parents and educators from 13 countries. To increase program sustainability, partner implementation stipends were reduced from 2018, and the program length reduced from 15 to 10 weeks. These changes resulted in decreased participation (-14%), retention (-6%) and learning gains; the latter was most likely due to participants’ higher socio-economic levels with greater access to knowledge and technology. Successes included identifying factors for effective mentor engagement to provide social and technical capital and deployment of an effective curriculum on responsible AI inventions.
An Effective 2-Dimension Graph Partitioning for Work Stealing Assisted Graph Processing on Multi-FPGAs

Multi-FPGA architectures have gained great interests in accelerating large-scale graph processing with great potential on high throughput and energy efficiency. As a beneficial complement, work stealing functions effectively to balance the computational workload on different FPGAs dynamically. Unfortunately, existing graph partitioning schemes originally designed in distributed settings potentially mismatch the work stealing-enabled multi-FPGA situations, where the computation is balanced while the communication overhead is unprecedentedly significant. In this paper, we present a 2-dimension balanced graph partitioning for work stealing assisted graph systems on multi-FPGAs, which can reduce communication overhead while preserving the optimal performance of work stealing. Our approach is novel by 1) exploring the tradeoff between load balance dimension and communication dimension in work-stealing-enabled graph processing system for the optimal performance, and 2) optimizing the memory access sequences to improve the granularity of graph partitioning for high-throughput graph analytics. Our experimental results show that our approach achieves 1.63x <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 2.56x speedups compared with state-of-the-art FPGA-based graph processing systems.
Digital contact tracing: technologies, shortcomings, and the path forward

Since the start of the COVID-19 pandemic, technology enthusiasts have pushed for digital contact tracing as a critical tool for breaking the COVID-19 transmission chains. Motivated by this push, many countries and companies have created apps that enable digital contact tracing with the goal to identify the chain of transmission from an infected individual to others and enable early quarantine. Digital contact tracing applications like AarogyaSetu in India, TraceTogether in Singapore, SwissCovid in Switzerland, and others have been downloaded hundreds of millions of times. Yet, this technology hasn't seen the impact that we envisioned at the start of the pandemic. Some countries have rolled back their apps, while others have seen low adoption [12, 17]. Therefore, it is prudent to ask what the technology landscape of contact-tracing looks like and what are the missing pieces. We attempt to undertake this task in this paper. We present a high-level review of technologies underlying digital contact tracing, a set of metrics that are important while evaluating different contact tracing technologies, and evaluate where the different technologies stand today on this set of metrics. Our hope is two fold: (a) Future designers of contact tracing applications can use this review paper to understand the technology landscape, and (b) Researchers can identify and solve the missing pieces of this puzzle, so that we are ready to face the rest of the COVID-19 pandemic and any future pandemics. A majority of this discussion is focused on the ability to identify contact between individuals. The questions of ethics, privacy, and security of such contact tracing are briefly mentioned but not discussed in detail.
Sharing Multi-User VR Spaces

In shared virtual reality (VR) spaces, users can appear and disappear without transitions, in a pop-in, pop-out style. This solution to entry and exit might be an acceptable solution for certain settings, but produces uncertainty in more intimate settings. Our research explores alternate entry and exit paradigms for shared VR spaces. Based on anthropological studies of entry and exit rituals in physical homes, we created a user journey map for entry and exit that differentiates the roles of host and guest in the shared VR space. We iteratively developed Unity prototypes that allowed a "host'' to invite a "guest'' into their virtual space, engage them in simple interactions, and invite the guest to depart. We define exvitation as a critical stage in the guest's departure--a social permission for a guest to depart the host's home. We identify exvitation prompts that were salient to our users and report of how these prompts were received by participants in our playtests. Future work on our user journey map as well as our exvitation prompts can aid in the expansion of playful experiences possible in VR.
Fall Detection Based on Local Peaks and Machine Learning

This research focuses on Fall Detection (FD) using on-wrist wearable devices including tri-axial accelerometers performing FD autonomously. This type of approaches makes use of an event detection stage followed by some pre-processing and a final classification stage. The event detection stage is basically performed using thresholds or a combination of thresholds and finite state machines. In this research, we extend our previous work and propose an event detection method free of thresholds to tune or adapt to the user that reduces the number of false alarms; we also consider a mixture between the two approaches. Additionally, a set of features is proposed as an alternative to those used in previous research. The classification of the samples is performed using a Deep Learning Neural Network and the experimentation performs a comparison of this research to a published and well-known technique using the UMA Fall, one of the publicly available simulated fall detection data sets. Results show the improvements in the event detection using the new proposals.
QR CODES IN THE PROCESS OF TEACHING HISTORY:OPPORTUNITIES, PROBLEMS AND PROSPECTS FOR APPLICATION IN UZBEKISTAN

The article analyzes the possibilities, problems and prospects of using QR codes in teaching history in the 21st century, when digital technology is booming. One of the ideas of the article is the importance of digitizing the higher education system in Uzbekistan.
Application of the Certainty Factor and Forward Chaining Methods to a Goat Disease Expert System

Goats are livestock that is financially very attractive to rural Indonesian. Efforts to solve problems related to goat farming are necessary. One of them is maintaining the health of the cattle by knowing how to cope with disease-stricken goats. Goat productivity will decrease if the treatment of the disease is sub-optimal. Goat diseases are very diverse, ranging from mild to severe. Breeders themselves can traditionally treat several diseases without the involvement of veterinarians or experts. However, a larger number of diseases need treatment with the help of experts. Expert systems are a potential solution to help farmers. It will automatically suggest decisions or conclusions in solving a problem. This study observes an expert system built using the Certainty Factor combined with Forward-Chaining. By combining the two methods, the information generated may discover the type of disease and suggest its management effectively with a high degree of certainty. The system can expectedly become a reference for goat breeders to consult about their goat livestock diseases. The knowledge base of the system uses 21 types of symptoms, eight types of diseases, and their solutions. The user does not need to input the belief value and the disbelief value that is usually input in the expert system. By involving the admin as a knowledge base processor, the correctness of the conveyed information maintains.
Multi-type Source Code Defect Detection Based on TextCNN

Classical static analysis for defect detection usually depends on code compilation or virtual compilation, which meanwhile requires high professional experience. The existing deep learning-based code defect analysis methods are mostly binary classification methods, in which the type of defects cannot be accurately judged. Under the condition of without compiling the project, a new multi-classification method was proposed which can achieve multi-type, cross function and cross file defect detection of source code. The core parts were word2vec and TextCNN, which were training based on abstract syntax tree, code attribute graph and program slices. The public data set SARD and open source projects were used to verify the performance of the method. The results show that this method is able to tackle 60 categories of source code defect classification task, which is superior to the existing methods in terms of model evaluation parameter precision, recall and F1 by 95.3%, 84.7%, 89.7% respectively.
A Heuristic Method for Automatic Gaze Detection in Constrained Multi- Modal Dialogue Corpora

We describe a heuristic-based approach to determining gaze allocation automatically in a multi-modal task oriented dialogue corpus. We present the development of the system and the evaluation of its performance and discuss the findings, including the shortcomings and the perspectives of the implemented approach.
Ground Truth Spanish Automatic Extractive Text Summarization Bounds

The textual information has accelerated growth in the most spoken languages by native Internet users, such as Chinese, Spanish, English, Arabic, Hindi, Portuguese, Bengali, Russian, among others. It is necessary to innovate the methods of Automatic Text Summarization (ATS) that can extract essential information without reading the entire text. The most competent methods are Extractive ATS (EATS) that extract essential parts of the document (sentences, phrases, or paragraphs) to compose a summary. During the last 60 years of research of EATS, the creation of standard corpus with human-generated summaries and evaluation methods which are highly correlated with human judgments help to increase the number of new state of the art methods. However, these methods are mainly supported for the English language, leaving aside other equally important languages such as Spanish, which is the second most spoken language by natives and the third most used on the Internet. A standard corpus for Spanish EATS (SAETS) is created to evaluate the state-of-the-art methods and systems for the Spanish language. The main contribution consists of a proposal for configuration and evaluation of 5 state-of-the-art methods, five systems and four heuristics using three evaluation methods (ROUGE, ROUGE-C, and Jensen-Shannon divergence). It is the first time that Jensen-Shannon divergence is used to evaluate AETS. In this paper the ground truth bounds for the Spanish language are presented, which are the heuristics baseline:first, baseline:random, topline and concordance. Also, the ranking of 30 evaluation test of the state-of-the-art methods and systems is calculated that forms a benchmark for SAETS.
A Simple Classification Ensemble for ADL and Falls

Fall Detection (FD) and ADL (Activity of Daily Living) identification is one the main challenges in a lot of real-world problems like work monitoring, healthcare systems, etc. Up to our knowledge, there are a lot of proposals in the literature for both problems separately, but few of them pose both problems at a time. A possible solution relies on-wrist wearable devices including tri-axial accelerometers performing ADL and Fall identification autonomously. Since the dynamics of both kind of activities (FALL and ADL) are quite similar and not easy to identify, mainly in FALL and high ADLs like Running, Jogging, GoUpstairs, etc, a technique considering peaks is suitable. Thus, in this study, an ensemble between KMEANS and KNN (stands for EKMEANS) taking as input a 19 features dataset calculated from a time window whenever a peak is detected. As peak detection algorithm is used, the MAX-PEAKS algorithm presented in [15]. The proposal is evaluated using the UMA Fall, one of the publicly available simulated fall detection data sets, and compared to two classical well-known algorithms: the KNN and a Feed Forward Neural Network (NN) [15]. The results show that our proposal outperforms the NN results. Future work includes a further analysis of the dynamics of the ensemble EKMEANS and a study of this problem using Deep-Learning.
Reflector based smart solar tracker (RBSST) as a solution of global climate change and its economic analysis for 900VA customer

One of the technical disadvantages of solar panels is having low power efficiency. Modifying its mechanical system and adding an automated system can be one way to increase the output power of solar panels. This study initiated the manufacture of solar trackers with a static reflectors and using a modified PID control method. The reflector is used to reflect light to the surface of the solar panel, so it can increase the input power of the solar panel. Based on the test results, the aluminum foil reflector with a slope angle of 70ᶿ can increase the solar panel output power by 3.2% higher than the flat mirror reflector. This solar panel is equipped with smart solar tracking system. The smart system in this research is using the difference between the reading of the light sensor on the top and bottom of the solar panel. Assuming when the difference is equal to zero the position of sunlight is right in the middle of the solar panel. When the difference is not equal to zero there will be an error, since this system includes the single input single output category therefore the PID system can be applied. Modifications are made to the generated PID control signal by adding a nested if condition to adjust the discrete actuator characteristics. The used PID constants are: Kp = 6, Ki = 20 and Kd = 1. The combination of static reflector and modified PID system produce an average output power of 5.75 Watts while static solar panels are 3.98 Watts. These results indicate that the addition of solar trackers and reflectors has succeeded in increasing electric power by an average of 44.47% higher compared to static solar panels. The results of the study also managed to outperform previous studies which only produced a power difference of 6.79%. The results of the economic analysis shows this system is quite competitive when applied to electricity consumers with 900VA power requirements.
Recursive Top-Down Production for Sentence Generation with Latent Trees

We model the recursive production property of context-free grammars for natural and synthetic languages.To this end, we present a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves, allowing us to compute the likelihood of a sequence of N tokens under a latent tree model, which we maximise to train a recursive neural function.We demonstrate performance on two synthetic tasks: SCAN (Lake and Baroni, 2017), where it outperforms previous models on the LENGTH split, and English question formation (McCoy et al., 2020), where it performs comparably to decoders with the ground-truth tree structure.We also present experimental results on German-English translation on the Multi30k dataset (Elliott et al., 2016), and qualitatively analyse the induced tree structures our model learns for the SCAN tasks and the German-English translation task.
Why Design Apps for Kids?

If you are reading this book, you probably already have your own reasons as to why you want to design apps for kids. Maybe you already work in the field and want to hear another point of view or have a broader vision on topics outside of your day-to-day. Perhaps you are considering starting a business in this industry and you want to know the basics, or, why not, you’re a parent that just wants to make informed choices about the products your child uses.