Reviewerly/exHarmony huggingface dataset:
Prospects of Robots in Assisted Living Environment

From caretaking activities for elderly people to being assistive in healthcare setup, mobile and non-mobile robots have the potential to be highly applicable and serviceable. The ongoing pandemic has shown that human-to-human contact in healthcare institutions and senior homes must be limited. In this scenario, elderlies and immunocompromised individuals must be exclusively protected. Robots are a promising way to overcome this problem in assisted living environments. In addition, the advent of AI and machine learning will pave a way for intelligent robots with cognitive abilities, while enabling them to be more aware of their surroundings. In this paper, we discuss the general perspectives, potential research opportunities, and challenges arising in the area of robots in assisted living environments and present our research work pertaining to certain application scenarios, i.e., robots in rehabilitation and robots in hospital environments and pandemics, which, in turn, exhibits the growing prospects and interdisciplinary nature of the field of robots in assisted living environment.
Design of High-Dimensional Grassmannian Frames via Block Minorization Maximization

In this letter, we present an iterative algorithm for constructing high-dimensional incoherent Grassmannian Frames (GFs) which are sets of unit-norm vectors that are minimax optimal: the maximum absolute value of their inner products is a minimum. We formulate the GF design as a nonconvex optimization problem and solve it via an efficient iterative algorithm. The bulk of each iteration of the proposed algorithm is a Linear Program (LP) for which there are a host of efficient solvers. The proposed algorithm is based on the theoretically sound Minorization Maximization technique which, unlike some of the state-of-the-art design approaches, monotonically minimizes the design criterion and can construct GFs with low coherence values.
An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.
Non-parametric Online Learning from Human Feedback for Neural Machine Translation

We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system. However, previous methods require online model updating or additional translation memory networks to achieve high-quality performance, making them inflexible and inefficient in practice. In this paper, we propose a novel non-parametric online learning method without changing the model structure. This approach introduces two k-nearest-neighbor (KNN) modules: one module memorizes the human feedback, which is the correct sentences provided by human translators, while the other balances the usage of the history human feedback and original NMT models adaptively. Experiments conducted on EMEA and JRC-Acquis benchmarks demonstrate that our proposed method obtains substantial improvements on translation accuracy and achieves better adaptation performance with less repeating human correction operations.
Efficient machine learning on data science languages with parallel data summarization

Nowadays, data science analysts prefer “easy” high-level languages for machine learning computation like R and Python, but they present memory and speed limitations. Also, scalability is another issue when the data set size grows. On the other hand, acceleration of machine learning algorithms can be achieved with data summarization which has been a fundamental technique in data mining. With these motivations in mind, we present an efficient way to compute the statistical and machine learning models with parallel data summarization that can work with popular data science languages. Our summarization produces one or multiple summaries, accelerates a broader class of statistical and machine learning models, and requires a small amount of RAM. We present an algorithm that works in three phases and is capable to handle data sets bigger than the main memory. Our solution evaluates a vector–vector outer product with C++ code to escape the bottleneck of the high-level programming languages. We present an experimental evaluation section with a prototype in the R language where the summarization is programmed in C++. Our experiments prove that our solution can work on both data subsets and full data set without any performance penalty. Also, we compare our solution (R combined with C++) with other parallel big data systems: Spark (Spark-MLlib library), and a parallel DBMS (similar approach implemented with UDFs and SQL queries). We show our solution is simpler and mostly faster than Spark based on the storage of the data set, and it is much faster than a parallel DBMS regardless of the storage of the data set.
Sustainable Education: Using Social Networks in Education for Change

Creating societies of the future goes hand in hand with promoting sustainable education and, therefore, universities must train educators who, through their own professional development, put into practice methodologies that are active, participative, and focused on the overall development of their students. The use of methodologies like Design Thinking and the use of social networks generate learning dynamics that bring into play key competences in the development of dedicated future educators. In this study, 156 students from Education degrees at the University of Jaen analyze how the use of these methodologies supports the learning process in dimensions such as creativity, motivation, communication, and involvement in learning, among others. The results shows that all dimensions correlate positively, and that the benefits derived from the use of these methodologies are perceived by students as greater than the effort required to put them into practice.
Hourly global horizontal irradiance data of three stations in Punjab, Pakistan

This paper presents hourly Global Horizontal Irradiance (GHI) measured data at three stations (Lahore, Multan, Bahawalpur) in Punjab, Pakistan. The estimated GHI data from three reanalysis datasets have also been presented. Clearness index (KT), Solar zenith angle (θsza) and Periodicity factor (Pf) were calculated and used to develop bias correction models. The estimated corrected GHI data using best model M20 for years 2015 and 2016 is also presented.
StegoFrameOrder—MAC Layer Covert Network Channel for Wireless IEEE 802.11 Networks

The proposed StegoFrameOrder (SFO) method enables the transmission of covert data in wireless computer networks exploiting non-deterministic algorithms of medium access (such as the distributed coordination function), especially in IEEE 802.11 networks. Such a covert channel enables the possibility of leaking crucial information outside secured network in a manner that is difficult to detect. The SFO method embeds hidden bits of information in the relative order of frames transmitted by wireless terminals operating on the same radio channel. The paper presents an idea of this covert channel, its implementation, and possible variants. The paper also discusses implementing the SFO method in a real environment and the experiments performed in the real-world scenario.
Coarse-To-Fine Segmentation of Organs at Risk in Nasopharyngeal Carcinoma Radiotherapy

Accurate segmentation of organs at risk (OARs) from medical images plays a crucial role in nasopharyngeal carcinoma (NPC) radiotherapy. For automatic OARs segmentation, several approaches based on deep learning have been proposed, however, most of them face the problem of unbalanced foreground and background in NPC medical images, leading to unsatisfactory segmentation performance, especially for the OARs with small size. In this paper, we propose a novel end-to-end two-stage segmentation network, including the first stage for coarse segmentation by an encoder-decoder architecture embedded with a target detection module (TDM) and the second stage for refinement by two elaborate strategies for large- and small-size OARs, respectively. Specifically, guided by TDM, the coarse segmentation network can generate preliminary results which are further divided into large- and small-size OARs groups according to a preset threshold with respect to the size of targets. For the large-size OARs, considering the boundary ambiguity problem of the targets, we design an edge-aware module (EAM) to preserve the boundary details and thus improve the segmentation performance. On the other hand, a point cloud module (PCM) is devised to refine the segmentation results for small-size OARs, since the point cloud data is sensitive to sparse structures and fits the characteristic of small-size OARs. We evaluate our method on the public Head&Neck dataset, and the experimental results demonstrate the superiority of our method compared with the state-of-the-art methods. Code is available at https://github.com/DeepMedLab/Coarse-to-fine-segmentation.
A Large-Scale Study of Machine Translation in Turkic Languages

Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted for building competitive systems.However, there is still a large number of languages that are yet to reap the benefits of NMT.In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios.In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 2 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores.All of our data, software and models are publicly available. 1
Enhanced Evaluation Model of Security Strength for Passwords Using Integrated Korean and English Password Dictionaries

In the field of information security, passwords are a means of authenticating users. Passwords with weak security cannot perform the role of user authentication and personal information protection because confidentiality is easily violated. To ensure confidentiality, it is important to evaluate the strength of the password and choose a very secure password. Due to this fact, security evaluation models for various passwords have been presented. However, existing evaluation models evaluate security based on the English alphabet. Passwords depend on the memory of the user and are closely related to the language or environment used by the user. In this regard, there are limitations in applying the existing security evaluation models to passwords chosen by non-English speakers. We compose a non-English, Korean language-based password dictionary and propose a password security evaluation model based on this for Korean users. In addition, to verify the effectiveness of the proposed model, we conducted experiments to evaluate the security of Korean language-based passwords using a database of passwords that have been actually leaked. As a result, the proposed model showed 99.38% accuracy for Korean language-based leaked passwords. This is superior to the 80.06% accuracy shown by the existing model. In conclusion, the use of the Korean language-based password security evaluation model proposed in this paper will contribute to choosing more secure passwords for Korean language-based sites or users.
Color Image Encryption using AES and RSA

Information security is an important task on multimedia and communication world. During storing and sharing maintaining a strategic distance from the outsider access of information is the difficult one. There are many encryption algorithms that can provide data security. In this paper two of the encryption algorithms namely AES and RSA are implemented for color images. AES (Advanced Encryption Standard) is a symmetric key block cipher published in December 2001 by NSIT (National Institute of Standards and Technology). RSA (Rivest-Shamir-Adleman) is an asymmetric key block cipher. It uses two separate keys, one for encryption called the public key and other for decryption called the private key. Both the implementation and analysis are done in Matlab. The quality and security level of both the algorithms is analysed based on various criteria such as Histogram analysis, Correlation analysis, Entropy analysis, NPCR (Number of Pixel Change Rate), UACI (Unified Average Changing Intensity), PSNR (Peak Signal-to-Noise Ratio).
SRIN: A New Dataset for Social Robot Indoor Navigation

With the rapid development of the Internet, our life is inseparable from it. The introduction of “smart transportation” is the embodiment of the Internet + transportation system. In the near future, the Internet + transportation system will become a central issue for development. Extract real-life examples from real life and show the changes that the Internet + transportation system has brought to our travels. At the same time, this passage will describe the problems existing in the existing transportation system and propose countermeasures for the corresponding problems.
Increasing the Competence of Teachers in the Formation of Socio-Emotional Skills of Inclusive Classes Pupils

Th e article presents a study aimed at increasing the competence of teachers in the formation of social-emotional skills of inclusive classes pupils.Preliminary study showed a low level of teachers' awareness of strategies to infl uence the social-emotional sphere of pupils and the lack of purposeful and consistent action in this direction.Th e proposed program of increasing the competence of teachers was based on the stages of consistent development of social-emotional skills.Th e conducted training program demonstrated a signifi cant improvement in the ability of teachers to meaningfully develop social-emotional skills of inclusive classes pupils, which is an important factor for their success in social interaction and life in general.
Optimization of Brand Marketing Strategy of Intelligent Technology under the Background of Artificial Intelligence

With the progress of Internet technology and the rapid development of artificial intelligence, intelligent technology products rapidly grow up. However, influenced by many factors, the marketing status of this kind of products in recent years is not ideal, for example, big data leakage and other issues. Especially in recent years, because of the greater market competition, the marketing of intelligent technology brands is facing greater problems. The purpose of this study is to analyze the current situation of brand marketing of intelligent technology under the background of artificial intelligence and propose the optimization strategy of brand marketing of such products, so as to promote the continuous improvement of brand marketing effect of intelligent technology. Based on artificial intelligence and the concept of brand marketing, through intelligent technology brand marketing investigation and experiment, the intelligent technology brand marketing status and current problems are analyzed; from marketing, product type, and product price, three angle analysis of the factors that influence the marketing and Internet marketing, product quality, product pricing, and product development is put forward from the four angles of specific strategy of optimization of smart technology products’ brand marketing. The experimental results show that the year-on-year growth rate of smart technology brand turnover is decreasing at the present stage. The increase rate of the turnover in 2019 is only 3.24%, far lower than that in 2015, and the growth rate of the turnover decreases by about 33%. At present, there are many problems in the marketing strategy of intelligent technology brands, and the optimization of marketing strategy is an urgent problem to be solved.
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results

Recent transformer-based approaches demonstrate promising results on relational scientific information extraction.Existing datasets focus on high-level description of how research is carried out.Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers.Our novel graph annotation schema incorporates not only coarse-grained entity spans as nodes and relations as edges between them, but also fine-grained attributes that modify entities and their relations, for a total of 12,738 labels in the corpus.By including more label types and more than twice the label density of previous datasets, SciClaim captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with their qualifications, subtypes, and evidence.We extend work in transformer-based joint entity and relation extraction to effectively infer our schema, showing the promise of fine-grained knowledge graphs in scientific claims and beyond.
Digital Humanism: Epistemological, Ontological and Praxiological Foundations

It is said that the distinction between the human and the artificial gets more and more blurred. Some are in favour of that development, some in fear of it. Some say humanism is outdated, some say digitalisation is necessarily undesirable. The paper analyses those positions from the perspectives of epistemology, ontology and praxeology and give a fresh answer to the seemingly irreconcilable points of view. A new conception of Digital Humanism can conserve what are true values in humanism and, at the same time, shape digitalisation such that those values are cultivated.
Reading eye movements performance on iPad vs print using a visagraph

This study investigated reading comprehension, reading speed, and the quality of eye movements while reading on an iPad, as compared to printed text. 31 visually-normal subjects were enrolled. Two of the passages were read from the Visagraph standardized text on iPad and Print. Eye movement characteristics and comprehension were evaluated. Mean (SD) fixation duration was significantly longer with the iPad at 270 ms (40) compared to the printed text (p=0.04) at 260 ms (40). Subjects' mean reading rates were significantly lower on the iPad at 294 words per minute (wpm) than the printed text at 318 wpm (p=0.03). The mean (SD) overall reading duration was significantly (p=0.02) slower on the iPad that took 31 s (9.3) than the printed text at 28 s (8.0). Overall reading performance is lower with an iPad than printed text in normal individuals. These findings might be more consequential in children and adult slower readers when they read using iPads.
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation

Intermediate layer knowledge distillation (KD) can improve the standard KD technique (which only targets the output of teacher and student models) especially over large pre-trained language models.However, intermediate layer distillation suffers from excessive computational burdens and engineering efforts required for setting up a proper layer mapping.To address these problems, we propose a RAndom Intermediate Layer Knowledge Distillation (RAIL-KD) approach in which, intermediate layers from the teacher model are selected randomly to be distilled into the intermediate layers of the student model.This randomized selection enforces that all teacher layers are taken into account in the training process, while reducing the computational cost of intermediate layer distillation.Also, we show that it acts as a regularizer for improving the generalizability of the student model.We perform extensive experiments on GLUE tasks as well as on out-of-domain test sets.We show that our proposed RAIL-KD approach outperforms other state-of-the-art intermediate layer KD methods considerably in both performance and training-time.
Converting to inclusive online flipped classrooms in response to Covid-19 lockdown

The global Covid-19 pandemic caused havoc in higher education teaching routines and several residential institutions encouraged instructors to convert existing modules to flipped classrooms as part of an online, blended learning strategy. Even though this seems a reasonable request, instructors straightaway encountered challenges which include a vague concept of what an online flipped classroom entails within a higher education context, a lack of guidelines for converting an existing module, facilitating learner engagement as well as unique challenges for inclusion of all learners in a digitally divided developing country in Covid-19 lockdown. In order to respond, we embarked on a study to identify the distinguishing characteristics of flipped classrooms to understand the as-is and to-be scenarios using a systematic literature review. The characteristics were used to develop of design considerations to convert to an online flipped classroom for higher education taking our diverse learner profiles into account. We subsequently converted a short module in an information systems department and shortly report on our experience.