Reviewerly/exHarmony huggingface dataset:
English Machine Translation Model Based on Artificial Intelligence

Abstract Machine translation is a language processing method that is widely used at present. However, because the machine translation mechanism extremely relies on rigid grammatical rules, the system often has large understanding deviations in the process of processing the original text, and it is often difficult to accurately grasp the semantic features. In this context, the author proposed an optimization method for English machine translation which is based on artificial intelligence on the premise of weakening grammatical rules. The above method first analyzes the grammar rules of English, and then obtains the semantic Gaussian marginalization rectangular window function. These functions project a large amount of information entropy data of English semantics. In order to obtain the nonlinear spectral features of adaptive matching semantics, the semantic correlation factors of words are added to the text information entropy and information gain. Simulation results show that the proposed method achieves good machine translation effect and has good development space.
Documenting evidence of a reproduction of ‘is there a “golden” feature set for static warning identification? — an experimental evaluation’

We report here the use of the static analysis dataset generated by FindBugs in a recent EMSE '21 paper by Yang et al. The artifact reproduced is supervised models to perform static analysis based on a golden feature set as studied by Wang et al.
Recommender Systems in the Real Estate Market—A Survey

The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research.
HapticLock: Eyes-Free Authentication for Mobile Devices

Smartphones provide access to increasing amounts of personal and sensitive information, yet are often only secured using methods that are prone to observational attacks. We present HapticLock, a novel authentication method for mobile devices that uses non-visual interaction modalities for discreet PIN entry that is difficult to attack by shoulder surfing. A usability experiment (N=20) finds effective PIN entry in secure conditions: e.g., in 23.5s with 98.3% success rate for a four-digit PIN entered from a random start digit. A shoulder surfing experiment (N=15) finds that HapticLock is highly resistant to observational attacks. Even when interaction is highly visible, attackers need to guess the first digit when PIN entry begins with a random number, yielding a very low success rate for shoulder surfing. Furthermore, a device can be hidden from view during authentication. Our use of haptic interaction modalities gives privacy-conscious mobile device users a usable and secure authentication alternative for sensitive situations.
Towards an Attention Mechanism LSTM Framework for Human Action Recognition in Videos

Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. Attention mechanisms have become a very important concept within deep learning approach, their operation tries to imitate the visual capacity of people that allows them to focus their attention on relevant parts of a scene to extract important information. In this paper we propose a soft attention mechanism adapted to a base CNN–LSTM architecture. First, a VGG16 convolutional neural network extracts the features from the input video. Then an LSTM classifies the video into a particular class. To carry out the training and testing phases, we used the HMDB-51 and UCF-101 datasets. We evaluate the performance of our system using accuracy as an evaluation metric, obtaining 40.7% (base approach), 51.2% (with attention) for HMDB-51 and 75.8% (base approach), 87.2% (with attention) for UCF-101.
Who does SEO in Spain? A cybermetric methodology for the construction of company universes

Due to their widespread use, search engines, and specifically Google, are extremely important to attract qualified traffic and thereby achieve the commercial objectives of corporations. Search engine optimization (SEO) allows enhanced connection of corporate websites with their audiences through search engines, using certain keywords. This study describes a methodology to determine the most important Spanish companies offering SEO services to third parties, representing an exploratory study of applied cybermetrics for the construction of a universe of companies. One of the relevant results of this work is the design of an information retrieval strategy based on a statistical analysis of search terms and the performance of geolocated searches in all the provincial capitals and Spanish cities with more than 100,000 inhabitants. After cleaning and reviewing the unique URLs, it is concluded that there are 1,043 companies offering SEO services in Spain positioned in Google. These are basically companies with more than one person, operating from a single location, and that offer other services in their portfolio together with SEO. In addition to providing a starting point for future research on the SEO sector in Spain, this research proposes a methodology for the creation of study universes of brands or companies.
Poisoning the Unlabeled Dataset of Semi-Supervised Learning

Semi-supervised machine learning models learn from a (small) set of labeled training examples, and a (large) set of unlabeled training examples. State-of-the-art models can reach within a few percentage points of fully-supervised training, while requiring 100x less labeled data. We study a new class of vulnerabilities: poisoning attacks that modify the unlabeled dataset. In order to be useful, unlabeled datasets are given strictly less review than labeled datasets, and adversaries can therefore poison them easily. By inserting maliciously-crafted unlabeled examples totaling just 0.1% of the dataset size, we can manipulate a model trained on this poisoned dataset to misclassify arbitrary examples at test time (as any desired label). Our attacks are highly effective across datasets and semi-supervised learning methods. We find that more accurate methods (thus more likely to be used) are significantly more vulnerable to poisoning attacks, and as such better training methods are unlikely to prevent this attack. To counter this we explore the space of defenses, and propose two methods that mitigate our attack.
When Is a Genuine Multipartite Entanglement Measure Monogamous?

A crucial issue in quantum communication tasks is characterizing how quantum resources can be quantified and distributed over many parties. Consequently, entanglement has been explored extensively. However, there are few genuine multipartite entanglement measures and whether it is monogamous is so far unknown. In this work, we explore the complete monogamy of genuine multipartite entanglement measure (GMEM) for which, at first, we investigate a framework for unified/complete GMEM according to the unified/complete multipartite entanglement measure we proposed in 2020. We find a way of inducing unified/complete GMEM from any given unified/complete multipartite entanglement measure. It is shown that any unified GMEM is completely monogamous, and any complete GMEM that is induced by given complete multipartite entanglement measure is completely monogamous. In addition, the previous GMEMs are checked under this framework. It turns out that the genuinely multipartite concurrence is not as good of a candidate as GMEM.
Teaching Keyword Spotters to Spot New Keywords with Limited Examples

Learning to recognize new keywords with just a few examples is essential for personalizing keyword spotting (KWS) models to a user's choice of keywords. However, modern KWS models are typically trained on large datasets and restricted to a small vocabulary of keywords, limiting their transferability to a broad range of unseen keywords. Towards easily customizable KWS models, we present KeySEM (Keyword Speech EMbedding), a speech embedding model pre-trained on the task of recognizing a large number of keywords. Speech representations offered by KeySEM are highly effective for learning new keywords from a limited number of examples. Comparisons with a diverse range of related work across several datasets show that our method achieves consistently superior performance with fewer training examples. Although KeySEM was pre-trained only on English utterances, the performance gains also extend to datasets from four other languages indicating that KeySEM learns useful representations well aligned with the task of keyword spotting. Finally, we demonstrate KeySEM's ability to learn new keywords sequentially without requiring to re-train on previously learned keywords. Our experimental observations suggest that KeySEM is well suited to on-device environments where post-deployment learning and ease of customization are often desirable.
On the Linguistic and Pedagogical Quality of Automatic Question Generation via Neural Machine Translation

Allowing learners to self-assess their knowledge through questions is a well-established method to improve learning. However, many educational texts lack a sufficient amount of questions for self-studying. Hence, learners read texts passively, and learning becomes inefficient. To alleviate the lack of questions, educational technologists investigate the use of automatic question generators. However, the vast majority of automatic question generation systems consider English input texts only. Therefore, we propose a simple yet effective multilingual automatic question generator based on machine-translation techniques. We investigate the linguistic and pedagogical quality of the generated questions in a human evaluation study.
A Meta-Reinforcement Learning Approach to Process Control

Meta-learning is a branch of machine learning which aims to quickly adapt models, such as neural networks, to perform new tasks by learning an underlying structure across related tasks. In essence, models are being trained to learn new tasks effectively rather than master a single task. Meta-learning is appealing for process control applications because the perturbations to a process required to train an AI controller can be costly and unsafe. Additionally, the dynamics and control objectives are similar across many different processes, so it is feasible to create a generalizable controller through meta-learning capable of quickly adapting to different systems. In this work, we construct a deep reinforcement learning (DRL) based controller and meta-train the controller using a latent context variable through a separate embedding neural network. We test our meta-algorithm on its ability to adapt to new process dynamics as well as different control objectives on the same process. In both cases, our meta-learning algorithm adapts very quickly to new tasks, outperforming a regular DRL controller trained from scratch. Meta-learning appears to be a promising approach for constructing more intelligent and sample-efficient controllers.
Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study

Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick–hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information (r134=−0.973, 95% CI −0.981,−0.962, p&lt;0.001). The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners.
ECR-DBSCAN: An improved DBSCAN based on computational geometry

A new density based clustering algorithm ECR−DBSCAN based on DBSCAN, has been presented in this paper. Computational geometry is applied to develop the modified DBSCAN algorithm. It is well known that the quality of density based clustering depends on its input parameters. However, it is not easy to determine proper values of input parameters for DBSCAN. This paper presents three significant modifications or extensions to DBSCAN related with (i) selection of hyper parameter epsilon (eps) using the radii of empty or voronoi circles (ii) selection of parameter minPoints (mp) for the same epsilon and (iii) redistribution of noise points to suitable clusters using the concept of centroid hinged clustering. ECR−DBSCAN is implemented with PYTHON accompanied by extensive experiment on benchmark data sets. Our experimental results establish the novelty and validity of the proposed clustering method over standard techniques.
Attention-Based Keyword Localisation in Speech Using Visual Grounding

Visually grounded speech models learn from images paired with spoken captions. By tagging images with soft text labels using a trained visual classifier with a fixed vocabulary, previous work has shown that it is possible to train a model that can detect whether a particular text keyword occurs in speech utterances or not. Here we investigate whether visually grounded speech models can also do keyword localisation: predicting where, within an utterance, a given textual keyword occurs without any explicit text-based or alignment supervision. We specifically consider whether incorporating attention into a convolutional model is beneficial for localisation. Although absolute localisation performance with visually supervised models is still modest (compared to using unordered bag-of-word text labels for supervision), we show that attention provides a large gain in performance over previous visually grounded models. As in many other speech-image studies, we find that many of the incorrect localisations are due to semantic confusions, e.g. locating the word 'backstroke' for the query keyword 'swimming'.
Comparing CTC and LFMMI for Out-of-Domain Adaptation of wav2vec 2.0 Acoustic Model

In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the overfitting issues with connectionist temporal classification (CTC) training to reduce its performance gap with flat-start lattice-free MMI (E2E-LFMMI) for automatic speech recognition with limited training data. Towards that objective, we use the pretrained wav2vec 2.0 BASE model and fine-tune it on three different datasets including out-of-domain (Switchboard) and cross-lingual (Babel) scenarios. Our results show that for supervised adaptation of the wav2vec 2.0 model, both E2E-LFMMI and CTC achieve similar results; significantly outperforming the baselines trained only with supervised data. Fine-tuning the wav2vec 2.0 model with E2E-LFMMI and CTC we obtain the following relative WER improvements over the supervised baseline trained with E2E-LFMMI. We get relative improvements of 40% and 44% on the clean-set and 64% and 58% on the test set of Librispeech (100h) respectively. On Switchboard (300h) we obtain relative improvements of 33% and 35% respectively. Finally, for Babel languages, we obtain relative improvements of 26% and 23% on Swahili (38h) and 18% and 17% on Tagalog (84h) respectively.
THE IMPACT OF DIGITAL TECHNOLOGIES ON LEARNING

The paper focuses on the impact of digital technologies on learning. Internet-based and new media education builds an educational model tailored to the needs of younger generations and offers an alternative to traditional education. The interest of analysis developed in this study is directed to understanding the existing relationships between the traditional education system and the modern one. The in-depth analysis of the new media issues used in the field of education leads us to the conclusion that the online user has a wider range of possibilities when accessing such systems. Compared to the traditional education system, the new media have two fundamental characteristics. First of all, interactivity. The user simultaneously accesses multiple online components, which gives different opportunities in this environment. Second, the concept of ubiquity. All the more, the impact of digital technologies on younger generations, using them from their young childhood, seems to be profound, with major reverberations about how they learn. Research data support, at least partially at this time, these premises. There is research that demonstrates without a doubt the positive impact of digital technologies on cognitive development, the formation of decision-making abilities, the diminishing of cognitive degenerative processes at the old age, but also the augmentation of fine motor skills. There are also many studies whose results are known in the public domain and are aimed at negative effects, starting with the risks of small children's exposure to digital technologies, which can lead to major attention deficits around the age of 7-8 years, and may continue to show the adverse effects of certain types of content conveyed in this environment, impacting, for example, the aggressiveness or perverse effects of social media on personal identity and self-esteem. The paper also presents the first software e-learning Romanian platform - Wand.education. Wand.education is a state-of-the-art software that provides educators with an intuitive platform for creating interactive educational content in a simple and fast way. The platform is intended for both teachers, pupils and parents who can create and access interactive educational content. In conclusion, digital society networks, the internet and new media are the most important challenge of the moment in the educational process, the generated impact becomes a peak of the progress made in all spheres of social life. Contemporary systems develop the task of continuous human training. The development of specialized competencies is a current issue, the educational curriculum directs the training and education process to achieve a quality training, suitable for an informational society.
The influence of music and educational songs on EFL students' achievement from their teachers' perspective in Jenin Region

Music and songs are part of the lives of all. They bring a lot of knowledge about the culture and identity from which they were produced, as a cultural phenomenon. They influence the emotional domain of the lives of individuals and give their minds a deeper insight (Vishnevskaia and Zhou, 2019). This paper desires to explore the influence of music and educational songs on EFL students' achievement from their teachers' perspective in the Jenin region. The research has been designed to probe the role of songs in teaching English to EFL students. Literary research on songs has helped to highlight the importance of Educational songs in teaching EFL students. In this respect, the researchers adopt a mixed-method (qualitative and quantitative). Likewise, data was collected through a questionnaire administered to English teachers and interviews of ten English teachers. The findings analysis shows that music and educational songs are barely used to develop learners’ language skills. The ongoing research work suggests that EFL teachers should be trained on the effective use of educational songs and music in English classes. Moreover, schools and curriculum should be provided with educational songs teaching materials.
A Context-Aware Hierarchical BERT Fusion Network for Multi-Turn Dialog Act Detection

The success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog acts and slot values in each turn. By treating utterances in isolation, most SLU systems often overlook the semantic context in which a dialog act is expected. The act dependency between turns is non-trivial and yet critical to the identification of the correct semantic representations. Previous works with limited context awareness have exposed the inadequacy of dealing with complexity in multiproned user intents, which are subject to spontaneous change during turn transitions. In this work, we propose to enhance SLU in multi-turn dialogs, employing a context-aware hierarchical BERT fusion Network (CaBERT-SLU) to not only discern context information within a dialog but also jointly identify multiple dialog acts and slots in each utterance. Experimental results show that our approach reaches new state-of-the-art (SOTA) performances in two complicated multi-turn dialogue datasets with considerable improvements compared with previous methods, which only consider single utterances for multiple intents and slot filling.
Pedestrian Detection by Fusion of RGB and Infrared Images in Low-Light Environment

Pedestrian detection in low-light environment is an essential part for autonomous driving in all-day and all-weather situations. A current trend is utilizing multispectral information such as RGB and infrared images to detect pedestrians. Despite its efficacy, such an approach suffers from underperformance in dealing with varied object scales due to its limited feature fusion on semantic levels. To address the above problem, we propose a novel multi-layer fusion network called as MLF-FRCNN. In this network, multi-scale feature maps are created from RGB and infrared channels from each backbone block. A feature pyramid network module is further introduced to facilitate predictions on multi-layer feature maps. The experimental results on the KAIST Dataset reveal that our method achieves a runtime performance of 0.14s per frame and an average precision of 91.2% which outperforms state-of-the-art multispectral fusion methods. The effectiveness of our approach in dealing with scaled objects in low-light environment is further proven by ablation studies.