Reviewerly/exHarmony huggingface dataset:
Application of Internet of Things Technology Based on Artificial Intelligence in Electronic Information Engineering

Artificial intelligence is an intelligent system that can perform tasks in a complex and changeable environment without special manual intervention. It is a technology that can continuously optimize decisions and behaviors through machine learning. It is a portrayal of human intelligence in computers. The simple explanation is usually “artificial intelligence.” Since the invention of artificial intelligence, theory and technology have been continuously developed and its scope has also been continuously expanded. With the improvement of people’s rich and colorful material living standards, more and more areas of life where artificial intelligence products are used can be widely and conveniently accessed by people, but the abuse of artificial intelligence technology will also become the biggest public safety in the future. Artificial intelligence technology is used by some terrorists, making it more convenient for terrorist attacks. To this end, this article aims to study the artificial intelligence Internet of Things technology in electronic information engineering. It is hoped that the artificial intelligence Internet of Things technology can be better optimized and the technical means can be mastered and rationally used to improve people’s material living conditions and happiness. This article proposes how to better apply the artificial intelligence Internet of Things technology to electronic information engineering and analyze optimization schemes and early warning models to better simulate life and optimize information conditions. The experimental results of this paper are analyzed through model construction. Finally, this paper selects and uses 16 standard mechanics datasets to find that it is beneficial to the better application of artificial intelligence Internet of Things technology, which verifies the accuracy and overall applicability of the method.
Exploring undergraduate students’ experiences in dealing with post-editing of machine translation

Although post-editing of machine translation (PEMT) has been much discussed and spoken among students in higher education institutions, little empirical evidence has been reported regarding the students’ real practices in their academic life. To fill this practical gap, this article explores the students’ experiences in dealing with PEMT in a Theoretical Foundation of Translating and Interpreting (TFTI) course. This exploration elaborates practical insights into how the students initially started practicing translation through PEMT in their actual day-to-day practices. Fifty-eight English students studying in an undergraduate program in a prominent public university in Indonesia voluntarily participated in this qualitative case study. Data were collected from students’ focused-group discussion (FGD), survey questionnaire, in-depth interviews, and students’ documents and the data were analyzed using thematic analysis. Findings showed that post-editing process provides the students with experiences in building their text knowledge to enable them to have high awareness and sensitivity to the academic texts. In addition, in PEMT practices, the students’ experiences were explored through the implementation of translation methods and techniques expected to build their awareness in PEMT with regard to language structure and language function. These experiences have given some empirical inputs for the lecturers to design relevant tasks and apply various translating practices for students’ more meaningful learning.
Hindi Multi-document Text Summarization using Text Rank Algorithm

Summarization is a technique in which the original text is summarized by selecting only what is important from the text.In today's world with the great advancement in the field of technology, huge amount of information is available online.In order to get only the necessary information from one or more sources available online, summarization is needed.By taking into the consideration the above problem, technique for hindi text summarization is proposed.Various documents related to sports, essays, news etc. which are available online were taken input for the system.The extraction of the summarized text was done on the basis of some important features of the text.It can be further used for news summary, summaries of books, etc.
Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios

In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable transceivers with embedded micro-controllers in four scenarios, namely indoor walking, outdoor walking, indoor jogging, and outdoor jogging. For brevity, we call it on-ankle path loss. The histogram of the on-ankle path loss showed clearly that there were two humps, where the second hump was closely related to the maximum path loss, which, in turn, corresponded to the step length. This histogram can be well approximated by a two-term Gaussian fitting curve model. Based on the histogram of the experimental data and the two-term Gaussian fitting curve, we propose a novel filtering technique to filter out the path loss outliers, which helps set up the upper and lower thresholds of the path loss values used for the step length estimation. In particular, the upper threshold was found to be on the right side of the second Gaussian hump, and its value was a function of the mean value and the standard deviation of the second Gaussian hump. Meanwhile, the lower threshold lied on the left side of the second hump and was determined at the point where the survival rate of the measured data fell to 0.68, i.e., the cumulative distribution function (CDF) approached 0.32. The experimental data showed that the proposed filtering technique resulted in high accuracy in step length estimation with errors of only 10.15 mm for the indoor walking, 4.40 mm for the indoor jogging, 4.81 mm for the outdoor walking, and 10.84 mm for the outdoor jogging scenarios, respectively.
Top-down and bottom-up attentional multiple instance learning for still image action recognition

We propose an end-to-end, top-down and bottom-up attentional deep multiple instance learning approach for still image action recognition. Our approach does not rely on neither any attribute nor object labels and/or explicitly trained object detectors. Instead, our approach simultaneously proposes image regions, chooses the action related regions with a top-down attention mechanism, weights each image region with a bottom-up attention layer to mask unrelated pixels to yield a fine-grained pixel-level action related map. Then, it embeds the selected regions into a single image-level feature descriptor and assigns an action label. We evaluate our approach on four benchmark still image action datasets: Stanford40 (Yao et al., 2011), Pascal VOC 2012 (Everingham et al., 2012) MPII (Andriluka et al., 2014) and VCOCO (Gupta and Malik, 2015). Our results demonstrate that, while increasing the overall recognition performance, our framework successfully selects action related image regions and creates pixel grade action masks.
Raising students' interest and deepening their training in acoustics through dedicated exercises

The aim of this paper is to briefly present ways to improve the interest of students who begin to attend physical acoustic courses and give examples of exercises (among many others) dedicated to the deepening of their training. Regarding the interest of students who are just beginning, two particular questions arise: how is the attention of these students gained? and how would the students be properly trained when starting out? An attempt to answer these questions is given at the beginning of the paper. Then, the paper continues with a brief presentation of some problems, which are extracted from a three-volume textbook that was published recently, precisely posed, and solved in detail analytically in an exact or approximate manner, even numerically, according to the three levels (bachelor, master, and Ph.D.) while offering an opportunity to deal extensively with the modeling of important topics in acoustics, which are treated as having several ways to be addressed.
A Systematic Literature Review: Acrophobia Treatment with Virtual Reality

Virtual Reality (VR) is becoming more popular as a treatment option for many phobias. We present a method that uses virtual reality in treatment to cure acrophobia in this research. Acrophobia is an anxiety condition that occurs when a person is exposed to heights. The goal of our study is to see whether Virtual Reality technology can be used to treat acrophobia. We conducted a comprehensive literature evaluation on publications relevant to acrophobia and virtual reality that were located on Google Scholar using a keyword-based search strategy. We noticed that the majority of papers involve more than two persons as a sample and use virtual reality exposure treatment. The participants' anxiety levels decrease with each exposure, suggesting that VR-based exposure might be employed as an acrophobic alternative treatment.
Sistem Pakar Mendiagnosa Penyakit Ayam Kampung Menggunakan Metode Certainty Factor

Animal husbandry is the activity of breeding and raising livestock to obtain the benefits and results of these activities. Activities in the field of animal husbandry can be divided into two groups, namely livestock farming of large animals such as cows, buffaloes and horses, while the second group is farming of small animals such as chickens, ducks, birds and others. Village chicken farming activities have existed since ancient times. Village chickens start from the first generation, namely from the descendants of the red jungle fowl (Gallus gallus). Raising free-range chickens is not easy because there are many things that must be considered and must have problems, therefore an expert system is needed to assist farmers in diagnosing and knowing the type of disease and symptoms experienced by the free-range chicken using a computer system automatically. This expert system uses the Certainty Factor method which aims to measure the level of certainty of facts or rules to describe an expert's belief in the problem at hand. This study shows how the process of calculating the Certainty Factor method in diagnosing free-range chicken disease with the initial combination rule to the last combination rule based on the selected symptoms, it can be concluded that the highest weight value is D01 Avian Influenza with a weight value of 0.99 x 100% = 99%
Implementations of Dijkstra Algorithm for Searching the Shortest Route of Ojek Online and a Fuzzy Inference System for Setting the Fare Based on Distance and Difficulty of Terrain (Case Study: in Semarang City, Indonesia)

Ojek Online is a motorcycle taxi that is usually used by people that need a short time for traveling.It is one of the easiest forms of transportation, but there are some obstacles in hilly areas such as Semarang City.The fare produced by online motorcycle taxis is sometimes not in accordance with the distance and difficulty of the terrain.The problems in this research are as follows: (1) How to find the shortest route of Ojek Online using the Dijkstra Algorithm, (2) How are the difficulties of the terrain along the shortest routes, (3) How to determine the fare using a fuzzy inference system (FIS) where the inputs are distance and the level of difficulty of the terrain on the geographical map in Semarang City area.The Dijkstra's Algorithm is used to assist in finding the shortest path and the Mamdani FIS is applied for fixing the fares.The results show that the Dijkstra Algorithm can find the shortest routes of ojek online from UNNES (initial node) to some destinantions in Semarang City.The result using fuzzy logic toolbox in matlab with the input of distance 5.5 km and terrain difficulty 300 m produced an output fare of 13,500 IDR.Further, the input of distance 5.5 km and the terrain difficulty of 100 m poduced an output fare of 13,100 IDR.Some routes with the same distance and different terrain heights have different fares.
Real time Bangla Digit Recognition through Hand Gestures on Air Using Deep Learning and OpenCV

Digit Recognition in real time through hand gestures has achieved great attention in machine learning and computer vision applications. This article focuses on identifying Bangla numerals in the air using hand motions. This research leads to the stairwell, allowing for more investigation in the same subject for various Bangla characters and even phrases. The major issue, however, is coping with the wide range of handwriting styles employed by various users. Many studies have been done on the identification of Bangla handwritten digits, but none has proven successful at recognizing Bangla digits in real time using hand gestures in the air. As a result, this article describes the creation of a Bangla digit recognition model that employs a Convolution Neural Network (CNN) to predict Bangla digits by observing hand movements in the air space. After a thorough examination, the suggested system attained a 98.37% accuracy on the BanglaLekha-Isolated dataset.
STRATEGI PENANGANAN BANJIR ROB KOTA PEKALONGAN

Pekalongan City is located in the lowlands of the northern coast of Java Island, experiencing tidal flooding with increasing frequency and area of inundation. Inundation due to tidal flooding that occurred in 2018 amounted to 1,391 hectares (29.97% of the total area) in 2020 increasing to 1,730 hectares (37.27% of the total area). The problem of tidal flooding occurred in 3 (three) sub districts covering 11 (eleven) Villages. National Medium-Term Development Plan (RPJMN) 2020-2024 mandates a major coastal protection project for 5 (five) urban areas including Greater Pekalongan. Various efforts have been made by various parties, including the Central Government, Central Java Provincial Government, Pekalongan City Government, Non-Governmental Organizations, Academics and other stakeholders to deal with the problem of tidal flooding in Pekalongan City. Seeing the dynamics of handling tidal floods in Pekalongan City carried out by various parties, it is necessary to research on strategies for handling tidal floods in Pekalongan City so that the handling carried out can be synergized and achieve targets effectively and efficiently. The purpose of this study was to formulate the strategy of tidal flood management in Pekalongan City and to determine the factors that influence. By analyzing Strengths, Weaknesses, Opportunities and Threats, the best strategy for handling tidal flooding in Pekalongan City will be obtained. The results of the study indicate the need for cooperation and coordination with various parties, community involvement and improvement of Human Resources to optimize the handling of tidal flooding in Pekalongan City.Keywords: Flood, tidal flood, Pekalongan
Spectare: Re-Designing a Stereoscope for a Cultural Heritage XR Experience

Stereoscopic photography was one of the main forms of visual communication in the second half of the 19th century. The experience of viewing stereoscopic photographs using stereoscopes is described as evoking memories of the past, feelings of presence in the depicted scenes, but also fun and magical experiences. The fact that using these devices generates these impactful experiences is relevant for Cultural Heritage (CH) where we want visitors to have memorable experiences. Since classic stereoscopes are similar to contemporary smartphone-based Virtual Reality (VR) viewers, we questioned how the original viewing experience could be re-imagined to take advantage of current technologies. We have designed a new smartphone-based VR device—Spectare—targeted towards experiencing CH content (2D or 360° photos or videos, soundscapes, or other immersive content), while still maintaining a user experience close to the original. In this paper, we describe the design process and operation of the Spectare device. We also report on an usability evaluation with 20 participants and on the field testing where we applied the device to the visualization of CH content resulting from a digital reconstruction of the monastery of Santa Cruz in Coimbra, Portugal. The evaluations uncovered issues with the smartphone support piece of the device, but generally its usage was classified with a high usability score. Participants also classified the device as innovative, creative, impressive, fun.
Two languages, one treebank: building a Turkish–German code-switching treebank and its challenges

Abstract This paper presents the SAGT Turkish–German code-switching treebank, and observations and annotation challenges we encountered during its development. The treebank consists of transcriptions of bilingual conversations annotated with several layers: language IDs, lemmas, POS tags, morphological features, and dependency relations. The annotations follow the Universal Dependencies annotation scheme and the conventions used in monolingual treebanks as much as possible. We present and discuss a number of issues that arise because of the need for consistent multilingual annotation within a single treebank, as well as the informal language, which is where code-switching is observed most. Besides proposing solutions to these issues, we present some observations about code-switching phenomena that are only possible to observe in a data set with rich linguistic annotation. The treebank was annotated with a focus on quality of annotations through an iterative process of detecting and correcting annotation errors. We also present quantitative measures for indication of annotation quality. The code-switching treebank created in this study is released to the public through Universal Dependencies repositories.
An Efficient Content-Based Video Recommendation

In a world full of online videos, it is really hard to find relevant content as the data is simply too much. A recommendation system was created to refine this experience, to match relevant content to an interested user. Most recommending systems use algorithms, calculations, and implicit feedback. These methods are effective unless the video does not have implicit feedback in which the algorithms will mostly fail to get relevant content. This is known as cold-start that affects newly uploaded videos, since they start without any data or user comments. Another problem facing users every day is finding the content they want, because it is dependent on videos having labels or having many user views. Since the search engine's mechanism uses the tags and keywords inserted for the video rather than the actual content in it. In this paper, a recommendation system by content is proposed, the system detects the objects and sounds inside the video, and also adds the feature to search using uploaded scenes or filter scenes based on keyword inputted. More experimental results have been done with various scenarios to demonstrate the effectiveness of the proposed system in terms of video recommendation by content
Hopping path planning in uncertain environments for planetary explorations

Abstract Hopping robots, called hoppers, are expected to move on rough terrains, such as disaster areas or planetary environments. The uncertainties of the hopping locomotion in such environments are high, making path planning algorithms essential to traverse these uncertain environments. Planetary surface exploration requires to generate a path which minimises the risk of failure and maximises the information around the hopper. This paper newly proposes a hopping path planning algorithm for rough terrains locomotion. The proposed algorithm takes into account the motion uncertainties using Markov decision processes (MDPs), and generates paths corresponding to the terrain conditions, or the mission requirements, or both. The simulation results show the effectiveness of the proposed route planning scheme in three cases as the rough terrain, sandy and hard ground environment, and non-smooth borders.
Multisensor Feature Fusion-Based Model for Business English Translation

With the wide use of computers, machine translation has been gradually applied in many fields from natural language processing, such as industry, education, and so on. Due to the increasing demand for multilanguage translation, it is an urgent problem to effectively improve the quality of text translation. Driven by the upsurge of artificial intelligence, neural network technology is increasingly integrated into the field of machine translation, which gradually expands the traditional machine translation method into neural machine translation method. With the continuous improvement of deep learning technology, machine translation has gradually integrated these methods and strategies and achieved good results in multiple tasks, but there are still some shortcomings. The most prominent problem is that, since word vector is the basis for the model to obtain semantic and grammatical information, the existing methods cannot obtain semantic and grammatical feature information, which greatly reduces the accuracy of English translation. Based on this, this paper proposed a method of splicing word vector with character- level and word-level encoding vector. The characterization of fusion of more word vector can effectively solve the word does not appear in the table, the word with some low frequency, can express meaning more complete information, performance directly affects the whole translation model, the results can be seen through the experiment, we put forward the characteristics of the fusion method and strategy, can effectively enhance the overall translation performance of the model.
IMPLEMENTING AN EFFICIENT DATA DEDUPLICATION FRAMEWORK FOR CLOUD STORAGE

As cloud computing dramatically increases, cloud service providers are under increased pressure to provide adequate data protection measures security.Cloud storage has become the recommended option to provide catastrophic recovery for users.In contrast to its convenience, customers are extremely worried that large-scale backup data would reduce storage costs.Data deduplication for backup storage is an effective approach.Current approaches for deduplicating cloud resources are not used to provide scalable backup service to cloud backup users and cannot fulfil the biased desire for various backup versions.For new versions of backup, consumers desire to minimize wait time by greater deduplicates and speed, but for older backups it is even more important to save storage costs.However, few studies take into account the problem of storage efficiency.This paper offers a new data deduction system with convergence encryption with blocking level data.The results of experiments demonstrate that the suggested system has an appropriate computational overhead and offers a potential option for data cloud storage and access scenarios in the real world.
Semi-Parametric Estimation Using Bernstein Polynomial and a Finite Gaussian Mixture Model

The central focus of this paper is upon the alleviation of the boundary problem when the probability density function has a bounded support. Mixtures of beta densities have led to different methods of density estimation for data assumed to have compact support. Among these methods, we mention Bernstein polynomials which leads to an improvement of edge properties for the density function estimator. In this paper, we set forward a shrinkage method using the Bernstein polynomial and a finite Gaussian mixture model to construct a semi-parametric density estimator, which improves the approximation at the edges. Some asymptotic properties of the proposed approach are investigated, such as its probability convergence and its asymptotic normality. In order to evaluate the performance of the proposed estimator, a simulation study and some real data sets were carried out.