Reviewerly/exHarmony huggingface dataset:
A Collection of Swedish Diachronic Word Embedding Models Trained on Historical Newspaper Data

This paper describes the creation of several word embedding models based on a large collection of diachronic Swedish newspaper material available through Språkbanken Text, the Swedish language bank. This data was produced in the context of Språkbanken Text’s continued mission to collaborate with humanities and natural language processing (NLP) researchers and to provide freely available language resources, for the development of state-of-the-art NLP methods and tools.
Web-Conference Lecturing: In Dialogue with Student Experiences During a Pandemic

Abstract covid -19 is an omnipresent feature of 2020, both globally and within Australia. For university students, a consequence of this has been the shift from on-campus to online delivery. Exploring these visual realities for lecturers and students, this article engages in Bakhtinian dialogism; a dialogic interaction that is born between peoples searching for meaning (Bakhtin, 1986). To do so, the authors engaged with and responded to students’ survey data whom they lecture and coordinate. Although the survey had limited responses, it enabled the authors to dialogue about received knowledge (istina) from students and contemplate this in relation to the authors’ own perspectives and experiences (pravda). Through this engagement, they suggest the importance of visually imbued emotive connectivity and dialogic relational care within web-conferencing, as well as didactic lecturing as valid forms of visual engagement.
The assessment of Indonesian EFL students' reading comprehension ability using Reading Evaluation and Decoding System (READS)

This study focused on reading comprehension ability of the English Department students in answering READS, MCQ questions. READS is an alternative standardized assessment system that is capable of measuring reading comprehension ability. This is a descriptive study based on Barrett's Taxonomy levels, such as literal comprehension, reorganization, and inferential comprehension. This study consisted of 122 students of the English Department academic year 2015 through 2018 at one of the private universities in Yogyakarta, Indonesia. This study analysis utilized the three Barrett's taxonomy scales to identify whether the students were at meet the standard, below standard, above standard or at academic warning. Finding of the research indicated that 27.8 % students categorized into academic warning (Band 2 and 3), 46.7% students categorized into below standard (Band 4) and 25.5% students categorized into meet standard (Band 5). Consequently, READS as an alternative assessment will guide the teachers to design specific reading material based on students' needs.
Exploration and Research on Web Programming Course in Higher Vocational College Under 1+X Certificate Pilot System

Shanghai Polytechnic University is one of the universities that carry out the web development 1+X certificate pilot system.1+X certificate pilot system has pointed out the direction for China's vocational education.Web programming course is the core course for web development 1+X certificate, which requires students to cooperate the knowledegs from several prerequisites together and highlights a lot on programming practice.Under the web development certificate system, the professional goals are in line with the requirements of web development engineers from IT industry.To meet the new requirements, some reforms should be carried out in our web programming course.Some novel ideas on teaching contents, teaching methods or modes and evaluation measures have been introduced into our course.Project-driven method is adoptted to train students practical abilities.At the same time, flipping class teaching mode is used in our teaching practice in order to make good use of students after-class time.From our teaching practice, these methods have been proved effectively.
A Novel Improved Reversible Visible Image Watermarking Algorithm Based on Grad-CAM and JND

With the rapid access convenience of content brought by 5G technology, the integrity protection of content becomes more important. The reversible visible watermarking algorithm has attracted more attention due to its effective content protection. In this paper, a novel improved reversible visible image watermarking scheme based on gradient-weighted class activation mapping (Grad-CAM) and the just noticeable difference (JND) model has been presented. The proposed region of interest (ROI) selection strategy is used to locate the main protected body of images for watermark embedding. Divide the watermark and ROI into nonoverlapping blocks in the same way and then embed the classified two types of watermark blocks into corresponding ROI blocks with the JND model. The optimal bit positions for watermark embedding can be selected adaptively with JND threshold and achieve the tradeoff between the watermark visibility and watermarked image quality. For lossless image recovery and watermark extraction, the recovery information is reversibly hidden into watermarked image. In the experiments, the same process of grayscale images is used to each channel separately for color images watermarking. Besides, there are six aspects in this paper to estimate the proposed scheme; with the comparison to other reversible visible watermarking schemes, experimental results demonstrate the effectiveness of our proposed scheme.
Profound Learning Approach for Shot Boundary Location

Distinguishing shot boundaries and Gradual shot change happen to be critical research areas in the ﬁeld of video retrieval, summarization and segmentation. Identification of Video shot boundaries is generally a significant and ﬁrst step for ordering, retrieval, video segmentation and event-based video analysis and numerous other such aspects. There has been extraordinary research to improve the accuracy of SBD calculations. Advance research on this work is reported towards interpretable highlights of edges. In this paper, we projected an identification of video shot structure dependent on Convolutional Neural Systems (CNNs). The method proposed in this paper uses RAI Dataset with the CNN network with improved results than traditional practices. We have tested the proposed system on the TRECVID IACC.3 dataset and made use of Keras and TensorFlow algorithms. Segmentation of videos in to fragment which extract video shots and separate the video shot boundary. This sort of preprocessing strategy helps in improving both the speed and precision of the SBD calculation. In the next part, shots are extracted through semantic mark which is produced during shot detection. The projected algorithm performs well when compared with the Adaptive algorithm in terms of recall, Precision, and F1 measure. The data set consists of standard videos (Movie Gods of Egypt, Tennis Sports, Bulletin news, Traffic Video playback, Krishna’s Carton).
DEff-ARTS: differentiable efficient ARchiTecture search

Manual design of efficient Deep Neural Networks (DNNs) for mobile and edge devices is an involved process which requires expert human knowledge to improve efficiency in different dimensions. In this paper, we present DEff-ARTS, a differentiable efficient architecture search method for automatically deriving CNN architectures for resource constrained devices. We frame the search as a multi-objective optimisation problem where we minimise the classification loss and the computational complexity of performing inference on the target hardware. Our formulation allows for easy trading-off between the sub-objectives depending on user requirements. Experimental results on CIFAR-10 classification showed that our approach achieved a highly competitive test error rate of 3:24% with 30% fewer parameters and multiply and accumulate (MAC) operations compared to Differentiable ARchiTecture Search (DARTS).
Assessment of Terrain and Land Use/Land Cover Changes of Mine Sites using Geospatial Techniques in Plateau State, Nigeria

In this paper, we report a geospatial assessment of the selected mine sites in the Plateau State, Nigeria. The aim of this work is to determine the impact of mining on the terrain as well as the Land Use/Land Cover (LULC) of the host communities. The Shuttle Radar Topographic Mission (SRTM) is used for the terrain mapping. The derived impact of mining on LULC between 1975 and 2014 is determined by classifying the relevant Landsat imageries. The digital terrain map reveal that the mining activity is not well-coordinated. Hence, the parts of the mine sites that are rich in the desired minerals are punctuated with low depth, while the other parts have high terrain as a result of the haphazard mining activity. The analysis of the LULC change show that the degraded land (DL), built-up area (BU), water bodies (WB), and exposed rock outcrop (RO) increase by 15.68%, 4.68%, 0.06%, and 14.5%, respectively, whereas the arable farmland (FL) and forest reserve (FR) decrease by 28.29% and 6.63%, respectively. Mining has adversely affected the natural ecology of the studied area. Therefore, the mine sites should be monitored, and their environmental damages should be pre-determined and mitigated. There should be regular inspections to keep these activities under control. The existing laws and regulations to conserve the natural ecosystems of the host communities should be enforced to curtail the excesses of the operators of the mining industries. Restoration of the minefields to reduce the existing hazards prevent further environmental degradation, and facilitating the socio-economic development of the area is also suggested.
A Text GAN for Language Generation with Non-Autoregressive Generator

Despite the great success of Generative Adversarial Networks (GANs) in generating high-quality images, GANs for text generation still face two major challenges: first, most text GANs are unstable in training mainly due to ineffective optimization of the generator, and they heavily rely on maximum likelihood pretraining; second, most text GANs adopt autoregressive generators without latent variables, which largely limits the ability to learn latent representations for natural language text. In this paper, we propose a novel text GAN, named NAGAN, which incorporates a non-autoregressive generator with latent variables. The non-autoregressive generator can be effectively trained with gradient-based methods and free of pretraining. The latent variables facilitate representation learning for text generation applications. Experiments show that our model is competitive comparing with existing text GANs in unconditional text generation, and it outperforms existing methods on sentence manipulation in latent space and unsupervised text decipherment.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms

We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games. While there has been significant innovation in MARL algorithms, algorithms tend to be tested and tuned on a single domain and their average performance across multiple domains is less characterized. Furthermore, since the hyperparameters of the algorithms are carefully tuned to the task of interest, it is unclear whether hyperparameters can easily be found that allow the algorithm to be repurposed for other cooperative tasks with different reward structure and environment dynamics. To investigate the consistency of the performance of MARL algorithms, we build an open-source library of multi-agent algorithms including DDPG/TD3/SAC with centralized Q functions, PPO with centralized value functions, and QMix and test them across a range of tasks that vary in coordination difficulty and agent number. The domains include the particle-world environments, starcraft micromanagement challenges, the Hanabi challenge, and the hide-and-seek environments. Finally, we investigate the ease of hyper-parameter tuning for each of the algorithms by tuning hyper-parameters in one environment per domain and re-using them in the other environments within the domain.
Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data

In this research paper, an incremental clustering approach-enabled MapReduce framework is implemented that include two phases, mapper and reducer phase. In the mapper phase, there are two processes, pre-processing and feature extraction. Once the input data is pre-processed, the feature extraction is done using wordnet features. Then, the features are fed to the reducer phase, where the features are selected using entropy function. Then, the automatic incremental clustering is done using bat-grey wolf optimizer (BAGWO). BAGWO is the integration of bat algorithm (BA) into grey wolf optimization (GWO) for generating various clusters of text documents. Upon the arrival of the incremental data, the mapping of the new data with respect to the centroids is done to obtain the effective cluster. For mapping, kernel-based deep point distance and for centroid update, fuzzy concept is used. The performance of the proposed framework outperformed the existing techniques using rand coefficient, Jaccard coefficient, and clustering accuracy with maximal values 0.921, 0.920, and 0.95, respectively.
A verified decision procedure for the first-order theory of rewriting for linear variable-separated rewrite systems

The first-order theory of rewriting is a decidable theory for finite left-linear right-ground rewrite systems, implemented in FORT. We present a formally verified variant of the decision procedure for the class of linear variable-separated rewrite systems. This variant supports a more expressive theory and is based on the concept of anchored ground tree transducers. The correctness of the decision procedure is verified by a formalization in Isabelle/HOL on top of the Isabelle Formalization of Rewriting (IsaFoR).
Information Protection in Dark Web Drug Markets Research

In recent years, there have increasingly been conflicting calls for more government surveillance online and, paradoxically, increased protection of the privacy and anonymity of individuals.Many corporations and groups globally have come under fire for sharing data with law enforcement agencies as well as for refusing to cooperate with said agencies, in order to protect their customers.In this study, we focus on Dark Web drug trading sites as an exemplary case of problematic areas of information protection, and ask what practices should be followed when gathering data from the Dark Web.Using lessons from an ongoing research project, we outline best practices for protecting the safety of the people under study on these sites without compromising the quality of research data gathering.
COVID-19 INDUCED HATE SPEECH AND AUSTIN’S SPEECH ACT THEORY PERSPECTIVE: IMPLICATIONS FOR PEACE BUILDING

Hate speech has become a matter of grave concern globally. It has continued to generate defamatory and stereotypical language which has perverted various platforms of information dissemination especially the social media. As the world is currently battling the COVID-19 pandemic, a lot of information on it is being disseminated via online platforms. Based on the COVID-19 „ infodemic‟ a lot of hate speech has emerged and language use has come to be at the centre of it all. Such use, from the Speech Act Theory perspective, elicits actions and reactions which most times are detrimental to people‟s harmonious existence. Using Austin‟s and Culpeper‟s models of Speech Act and Impoliteness Theory, respectively, this study analysed various data on COVID-19 related information shared on the social media to determine the level of toxicity of language use, and the effect on peace building amongst nations, Nigeria inclusive. Amongst other findings, this paper discovered that communications on the social media, in reaction to COVID-19 issues are laden with hate speeches which have given rise to instances of violence, unrest, stigmatization and racism against the target people. It recommends, amongst other things, that all social media/online platforms should put in place strict guidelines for users, in its fight against hate speech. It also advocates the inclusion of language and communication arts as a course of study in the tertiary education curriculum.
Disentangled Recurrent Wasserstein Autoencoder

Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework. However, only a few works have explored unsupervised disentangled sequential representation learning due to challenges of generating sequential data. In this paper, we propose recurrent Wasserstein Autoencoder (R-WAE), a new framework for generative modeling of sequential data. R-WAE disentangles the representation of an input sequence into static and dynamic factors (i.e., time-invariant and time-varying parts). Our theoretical analysis shows that, R-WAE minimizes an upper bound of a penalized form of the Wasserstein distance between model distribution and sequential data distribution, and simultaneously maximizes the mutual information between input data and different disentangled latent factors, respectively. This is superior to (recurrent) VAE which does not explicitly enforce mutual information maximization between input data and disentangled latent representations. When the number of actions in sequential data is available as weak supervision information, R-WAE is extended to learn a categorical latent representation of actions to improve its disentanglement. Experiments on a variety of datasets show that our models outperform other baselines with the same settings in terms of disentanglement and unconditional video generation both quantitatively and qualitatively.
A Discovery Method for New Words From Mobile Product Comments

A large number of new words in product reviews generated by mobile terminals are valuable indicators of the privacy preferences of customers. By clustering these privacy preferences, sufficient information can be collected to characterize users and provide a data basis for the research issues of privacy protection. The widespread use of mobile clients shortens the string length of the comment corpus generated by product reviews, resulting in a high repetition rate. Therefore, the effective and accurate recognition of new words is a problem that requires an urgent solution. Hence, in this paper, we propose a method for discovering new words from product comments based on Mutual Information and improved Branch Entropy. Firstly, by calculating the Co-occurrence Frequency and Mutual Information between words and adjacent words, the character strings of words after pre-processing and word segmentation are expanded left and right respectively to discover the potential word set. The candidate set of new words is obtained by means of an improved support filtering algorithm. Finally, a new word set is built by applying an improved Branch Entropy filtering algorithm and removing old words. The experimental results show that this method can accurately and effectively identify new words in product comments.
Posting Recommendations in Healthcare Q&amp;A Forums

Online Q&amp;A forums, unlike search engines, allow posting of various types of queries, thus attracting users to seek information and solve problems in specific domains. However, as insufficient knowledge leads to incomprehensible queries, unsuitable responses are common. We develop posting recommendation systems (RSs) to support users in composing reasonable posts and receiving effective answers. The posting RSs were evaluated by a user study containing 27 participants and three tasks to examine if users engaged more in the question generation process. Two medical experts were recruited to verify whether professionals can understand and answer posts supported by RSs. The results show that the proposed mechanism enables askers to produce posts with better understandability, which leads experts to devote more attention to answer their questions.
Encoding and Decoding of Barcodes for Grayscale and color images

Barcodes have prevailed in every field for ages easing the storing and retrieval process which otherwise was erroneous and tedious due to manual intervention. Increasing the domain's needs and easier storage and retrieval of data has demanded continuous enhancements in the barcodes. This paper proposes a novel technique to generate picture-embedded barcodes and decode them by novel pattern substitution method, thereby facilitating data storage into barcodes holding picture of data stored like person ID.Keywords-Barcodes, Symbology, Encoding, Decoding
Análisis prospectivo del uso de flipped classroom por el futuro profesorado de Lengua castellana y literatura en Secundaria

espanolEl objetivo principal de la presente investigacion es analizar la percepcion de un grupo de estudiantes del Master de Secundaria sobre el uso futuro de flipped classroom como docentes de Lengua castellana y literatura. Nuestro estudio, de tipo mixto –cualitativo y cuantitativo–, se ha llevado a cabo bajo el paradigma interpretativo con caracteristicas de investigacion-accion participativa. La muestra es no probabilistica, con estudiantes matriculados (n=38) en una asignatura obligatoria del programa. La recogida de datos se efectuo mediante un cuestionario validado, y estructurado en 5 items y 24 subitems, con preguntas abiertas y cerradas. Segun algunos resultados, la mayoria de los participantes (81.58%) cree que volveria a usar la clase invertida en su docencia futura por su utilidad y eficacia, dinamismo y agilidad, asi como por su capacidad motivadora. En cambio, la falta de formacion en produccion audiovisual y la escasa experiencia en sus primeros anos de docencia han sido los principales motivos aducidos por quienes afirman que no practicaran esta metodologia con su alumnado. La planificacion de una propuesta didactica de clase invertida suscito en los participantes un mayor convencimiento de la idoneidad de su uso, asi como una mayor consciencia de sus propias limitaciones para ello. EnglishThe main objective of the present study is to analyse the perceptions of Secondary Education Master’s Degree students as teachers of Spanish language and its literature for native speakers on the future use of the flipped classroom. This mixed qualitative and quantitative study was carried out under an interpretive paradigm that shares characteristics with participatory action research. A non-probabilistic sample was selected from the students enrolled in a compulsory module of the program (n = 38). The questionnaire was validated and structured in 5 items and 24 sub-items with open and closed questions. The majority of the participants (81.58%) believe that they will use the flipped classroom methodology. On the other hand, lack of training in audio-visual production and of experience in their first years of teaching were the main reasons given by those who affirm that they will not use this methodology with their students. The proposal of flipped classrooms aroused in the participants a greater conviction of the suitability of its use, as well as a greater awareness of their own limitations regarding this method.