Reviewerly/exHarmony huggingface dataset:
Evaluating the impact of female community health volunteer involvement in a postpartum family planning intervention in Nepal: A mixed-methods study at one-year post-intervention

This is a one-year post-intervention study following an initiative to provide orientation to female community health volunteers (FCHVs) on postpartum family planning in Nepal. In light of positive results in the earlier post-intervention study, this study was designed to provide a more long-term perspective on sustainability by assessing the effect at one-year post-intervention.This mixed-methods study was conducted in January 2020 in Morang district, Nepal. We collected quantitative data from a knowledge assessment of FCHVs who had participated in the intervention on postpartum family planning, data on their community-based counseling coverage and through interviews with postpartum mothers in two selected hospitals. Qualitative data were collected through six key informant interviews with health providers and four focus group discussions with FCHVs involved in the intervention. We performed descriptive and multivariate analyses for quantitative data and thematic analysis for qualitative data.In total, 206 FCHVs participated in the one-year post-intervention study with significant improvement in knowledge of postpartum family planning as compared to pre-intervention period. The adjusted odds ratios (AOR) for knowledge of the 5 key messages on postpartum family planning as compared to the pre-intervention period included 1) knowledge on postpartum family planning can be used immediately after birth (AOR = 18.1, P<0.001), 2) postpartum intra-uterine device (PPIUD) can provide protection up to 12 years (AOR = 2.9, P = 0.011), 3) mothers who undergo cesarean section can use PPIUD (AOR = 2.3, P<0.001), 4) PPIUD can be inserted immediately after birth (AOR = 6.2, P <0.001), and 5) women should go for follow-up immediately if the IUD strings are seen outside vulva (AOR = 2.0, P = 0.08). The FCHVs answering 4 or more questions correctly was 10 times higher (AOR = 10.1, P<0.001) at one-year post-intervention, whereas it was 25 times higher at immediate-post-test (AOR = 25.1, p<0.001) as compared to pre-intervention phase. The FCHVs had counseled 71% of the pregnant women (n = 538) within their communities at one-year post-intervention. The postpartum mothers in hospitals had a 2 times higher odds of being counseled by FCHVs during their pregnancy at one-year post-intervention (AOR = 1.8, P = 0.039) than in pre-intervention phase. The qualitative findings suggested a positive impression regarding the FCHV's involvement in postpartum family planning counseling in the communities, however, supervision and monitoring over a longer term was identified as a key challenge and that may influence sustainability of community-based and hospital-based postpartum family planning services.The FCHVs' knowledge and community-based activities on postpartum family planning remained higher than in the pre-intervention. However, it declined when compared to the immediate post-intervention period. We propose regular supervision and monitoring of the work of the FCHVs to sustain progress.
Staying at the front line of literature: How can topic modelling help researchers follow recent studies?

Staying at the front line in learning research is challenging because many fields are rapidly developing. One such field is research on the temporal aspects of computer-supported collaborative learning (CSCL). To obtain an overview of these fields, systematic literature reviews can capture patterns of existing research. However, conducting systematic literature reviews is time-consuming and do not reveal future developments in the field. This study proposes a machine learning method based on topic modelling that takes articles from a systematic literature review on the temporal aspects of CSCL (49 original articles published before 2019) as a starting point to describe the most recent development in this field (52 new articles published between 2019 and 2020). We aimed to explore how to identify new relevant articles in this field and relate the original articles to the new articles. First, we trained the topic model with the Results, Discussion, and Conclusion sections of the original articles, enabling us to correctly identify 74% (n = 17) of new and relevant articles. Second, clusterisation of the original and new articles indicated that the field has advanced in its new and relevant articles because the topics concerning the regulation of learning and collaborative knowledge construction related 26 original articles to 10 new articles. New irrelevant studies typically emerged in clusters that did not include any specific topic with a high topic occurrence. Our method may provide researchers with resources to follow the patterns in their fields instead of conducting repetitive systematic literature reviews.
AI Strict Liability Vis-À-Vis AI Monopolization

Some argue that applying a strict liability regime on AI-inflicted damages may allow well-financed big AI companies to monopolize the industry. They hypothesize that a strict liability regime would expose AI companies to significant legal liability. Since small AI companies lack the necessary resources to pay for damages inflicted by their AI technology, a strict liability regime could erect barriers to entry for these small companies. Ultimately, the argument continues, such a regime would give a small group of companies a virtual monopoly on the AI industry. Thus, some conclude that strict liability inherently stifles innovation and should not be applied to emerging technologies, such as AI.&#x0D; This Article maintains that legislators should adopt a strict liability regime, and it rejects the above argument for two reasons. First, there is no substantial connection between a strict liability regime and the AI monopolization that is already underway. Second, insurance policies could mitigate the effects a strict liability regime may have on the capabilities of small AI companies to enter and compete in this important market. Therefore, the ongoing process of monopolization of the AI market should not by itself render strict liability a non-viable regime when AI-inflicted damages occur.
Filipino Native Language Identification using Markov Chain Model and Maximum Likelihood Decision Rule

The study developed a tool for identification of a Filipino Native Language given a textual data. The Filipino Language identified were Cebuano, Kapampangan and Pangasinan. It used Markov Chain Model for language modeling using bag of words (a total of 35,144 words for Cebuano, 14752 for Kapampangan, and 13969 of Pangasinan) from each language and maximum likelihood decision rule for the identification of the native language. The obtained model implementing Markov model, was applied in one hundred fifty text files with minimum length of ten words and maximum length of fifty words. The result of the evaluation shows the system’s accuracy of 86.25% and an F-Score of 90.55%.
Strengthening Existing Internet of Things System Security: Case Study of Improved Security Structure in Smart Health

Sensor applications and Internet of Things (IoT) technology using many sensors and smart devices (IoT devices) have been commercially implemented and are significantly changing our daily lives.However, most IoT devices are vulnerable due to low power consumption and have inadequate physical security protection mechanisms.The information security protection of existing sensors is very limited, particularly when large numbers of smart devices are deployed in smart application systems.This limited protection is a major information security concern and has become an important personal privacy issue.The study of the IoT architecture and security taxonomy in the beginning of this paper will help readers understand our proposed concept for improving the security level of existing systems without taking down the whole deployed system, which is the key contribution of this article.Through an actual case study, we have found that by improving the network planning and security management mechanism and applying network segmentation, monitoring, filtering, and IoT trust connection, we can strengthen the security protection of existing IoT systems.We demonstrated that raising the security level of existing smart health systems will increase market value both now and in the future, and ad hoc IoT security solutions can be feasibly deployed in all sensor application fields.
A novel hybrid approach to detect brain tumor in MRI images

Image segmentation is the fundamental step in medical image analysis. Segmentation is a procedure to separate similar portions of images showing resemblance in different features such as color, intensity, or texture. Grayscale images are mostly used for the segmentation of medical images. Tumors are commonly stated as the abnormal growth of tissues and the brain tumor is a diseased part in the body tissues that is an abnormal mass in which the growth rate of cells is irrepressible. The mortality rate of people has raised over the past years due to brain tumors, hence this area has gained the attention of researchers. Automatic detection of brain tumors is a challenging task because it involves pathology, functional physics of MRI along with intensity and shapes analysis of MR image. After all, tumor shape, size, location, and intensity vary for each infected case. In this work, a novel hybrid approach is implemented by combing watershed segmentation, level set segmentation and K means clustering. First, the image is preprocessed by removing the skull. Watershed segmentation is applied to this preprocessed image. Level set segmentation is applied to the previous step. Finally, k means clustering is applied as the final step to detect tumor parts accurately. This Hybrid approach is compared with other four techniques such as Threshold segmentation, K means clustering, Watershed segmentation, and Level set-based segmentation methods. Statistical and Visual analysis is performed. It is found that the hybrid approach has better specificity, accuracy, and precision among all four techniques. Further, it is able to detect tumors more accurately. This research could help clinicians in surgical planning, treatment planning and accurately segmenting the tumor part with the most accurate method.
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples

Capturing word meaning in context and distinguishing between correspondences and variations across languages is key to building successful multilingual and cross-lingual text representation models.However, existing multilingual evaluation datasets that evaluate lexical semantics "in-context" have various limitations.In particular, 1) their language coverage is restricted to high-resource languages and skewed in favor of only a few language families and areas, 2) a design that makes the task solvable via superficial cues, which results in artificially inflated (and sometimes super-human) performances of pretrained encoders, and 3) no support for crosslingual evaluation.In order to address these gaps, we present AM 2 ICO (Adversarial and Multilingual Meaning in Context), a widecoverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs.We conduct a series of experiments in a wide range of setups and demonstrate the challenging nature of AM 2 ICO.The results reveal that current SotA pretrained encoders substantially lag behind human performance, and the largest gaps are observed for low-resource languages and languages dissimilar to English.
DETEKSI SERANGGA HAMA PADA GUDANG PENYIMPANAN BIJI PINANG (Areca catechu) MENGGUNAKAN BEBERAPA METODEPENGAMBILAN SAMPEL

The research aims to detect insect in betel nut storage (Areca catechu) using some sampling methods. The research was conducted by survey method using three sampling methods, namely hand sampling method, yellow sticky trap method, and dome trap method. The parameters observed were types of insect pests found, insect populations obtained, and abundance of pest insect populations. The research was conducted from July to October 2018 in four betel nut storagesin Jambi City and Muaro Jambi District. Insects found in the betel nut storages with three sampling methods were 28 pest species. In the hand sampling method found 16 types of pest insects, in the yellow sticky trap trap method found 21 types of insect pests, and in the dome trap method found 21 types of insect pests. The dominant insects found in the three sampling methods were Ahasverus advena, Araecerus fasciculatus, Callosobruchus spp., Carpophilus dimidiatus, Cryptolestes ferrugineus, Cryptolestes pusillus, Hypothenemus hampei, Lasioderma serricorne, Lophocateres pusillus, Oryzaephilus mercator, Typhaea stercorea, and Tribolium castaneum.
TS-PADM: Anomaly Detection Model of Wireless Sensors Based on Spatial-Temporal Feature Points

In the practical application, sensor data collection is an essential means for the system to perceive the intrinsic features of data. The anomaly detection of data points can improve data quality and explore the potential information of data. The anomaly detection can be classified as two basic types, that is, classification and clustering. Those methods usually depend on the spatial correlation of data and have high computation complexity, so they are not suitable for the smart home and another mini-Internet of Things (IoT) environment. To overcome these problems, we propose a novel method for anomaly detection. In this paper, we first define the temporal and spatial feature of data flows; then, a time series denoising autoencoder (TSDA) is proposed to extract the discriminative high-dimensional characteristics to represent the data points. Moreover, a probability statistics-based anomaly detection model (PADM) was proposed for identifying the abnormal data. Extensive experimental results demonstrated that our method has fewer parameters and is easy to adjust and optimize. More importantly, our approach has higher precision and recall rate than the gradient boosted decision tree and XGBoot.
PolyLM: Learning about Polysemy through Language Modeling

To avoid the "meaning conflation deficiency" of word embeddings, a number of models have aimed to embed individual word senses.These methods at one time performed well on tasks such as word sense induction (WSI), but they have since been overtaken by task-specific techniques which exploit contextualized embeddings.However, sense embeddings and contextualization need not be mutually exclusive.We introduce PolyLM, a method which formulates the task of learning sense embeddings as a language modeling problem, allowing contextualization techniques to be applied.PolyLM is based on two underlying assumptions about word senses: firstly, that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring; and secondly, that for a given occurrence of a word, one of its senses tends to be much more plausible in the context than the others.We evaluate PolyLM on WSI, showing that it performs considerably better than previous sense embedding techniques, and matches the current stateof-the-art specialized WSI method despite having six times fewer parameters.Code and pre-trained models are available at https:// github.com/AlanAnsell/PolyLM.
SLTEV: Comprehensive Evaluation of Spoken Language Translation

Automatic evaluation of Machine Translation (MT) quality has been investigated over several decades.Spoken Language Translation (SLT), especially when simultaneous, needs to consider additional criteria and does not have a standard evaluation procedure and a widely used toolkit.To fill the gap, we introduce SLTEV, an open-source tool for assessing SLT in a comprehensive way.SLTEV reports the quality, latency, and stability of an SLT candidate output based on the time-stamped transcript and reference translation into a target language.For quality, we rely on sacreBLEU which provides MT evaluation measures such as chrF or BLEU.For latency, we propose two new scoring techniques.For stability, we extend the previously defined measures with a normalized Flicker in our work.We also propose a new averaging of older measures.A preliminary version of SLTEV was used in the IWSLT 2020 SHARED TASK.Moreover, a growing collection of test datasets directly accessible by SLTEV are provided for system evaluation comparable across papers.
Evaluation Of The Efficacy Of Iot Deployment On Petro-Retail Operations

After the Internet revolution, Internet of Things (IoT) is expected to be the next big technological change, connecting equipment as well as everyday objects to help businesses explore new ways of creating and delivering value. In the Oil &amp; Gas industry context, IoT has been deployed extensively in the upstream sector and also to some extent in the mid-stream sector. However, it’s implementation in the downstream (retail) sector has been limited. This paper explores the applicability and impact of deploying an IoT solution at a fuel retail outlet. In our experiment, a live outlet was chosen in Kolkata, where a limited IoT solution was deployed, for a period of 3 months. Our research shows that IoT implementation can yield significant operational benefits by reducing the response times, leading to increased operational uptime and hence sales. It can also help improve the root cause analysis process by providing increased data to identify the issue(s).
A Novel Granularity Optimal Feature Selection based on Multi-Variant Clustering for High Dimensional Data

Clustering is the most complex in multi/high dimensional data because of sub feature selection from overall features present in categorical data sources. Sub set feature be the aggressive approach to decrease feature dimensionality in mining of data, identification of patterns. Main aim behind selection of feature with respect to selection of optimal feature and decrease the redundancy. In-order to compute with redundant/irrelevant features in high dimensional sample data exploration based on feature selection calculation with data granular described in this document. Propose aNovel Granular Feature Multi-variant Clustering based Genetic Algorithm (NGFMCGA) model to evaluate the performance results in this implementation. This model main consists two phases, in first phase, based on theoretic graph grouping procedure divide features into different clusters, in second phase, select strongly representative related feature from each cluster with respect to matching of subset of features. Features present in this concept are independent because of features select from different clusters, proposed approach clustering have high probability in processing and increasing the quality of independent and useful features.Optimal subset feature selection improves accuracy of clustering and feature classification, performance of proposed approach describes better accuracy with respect to optimal subset selection is applied on publicly related data sets and it is compared with traditional supervised evolutionary approaches
Automatic Data Acquisition for Event Coreference Resolution

We propose to leverage lexical paraphrases and high precision rules informed by news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles.We perform both manual validation and empirical evaluation on multiple evaluation datasets with different event domains and text genres to assess the quality of our acquired event pairs.We found that a model trained on our acquired event pairs performs comparably as the supervised model when applied to new data out of the training data domains.Further, augmenting human-annotated data with the acquired event pairs provides empirical performance gains on both in-domain and out-of-domain evaluation datasets.
Weighted Classification of Machine Learning to Recognize Human Activities

This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision.
On the evolution of syntactic information encoded by BERT’s contextualized representations

The adaptation of pretrained language models to solve supervised tasks has become a baseline in NLP, and many recent works have focused on studying how linguistic information is encoded in the pretrained sentence representations.Among other information, it has been shown that entire syntax trees are implicitly embedded in the geometry of such models.As these models are often fine-tuned, it becomes increasingly important to understand how the encoded knowledge evolves along the fine-tuning.In this paper, we analyze the evolution of the embedded syntax trees along the fine-tuning process of BERT for six different tasks, covering all levels of the linguistic structure.Experimental results show that the encoded syntactic information is forgotten (PoS tagging), reinforced (dependency and constituency parsing) or preserved (semanticsrelated tasks) in different ways along the finetuning process depending on the task.
Comprehensive Framework for Describing Interactive Sound Installations: Highlighting Trends through a Systematic Review

We report on a conceptual framework for describing interactive sound installations from three complementary perspectives: artistic intention, interaction and system design. Its elaboration was informed by a systematic review of 181 peer-reviewed publications retrieved from the Scopus database, which describe 195 interactive sound installations. The resulting taxonomy is based on the comparison of the different facets of the installations reported in the literature and on existing frameworks, and it was used to characterize all publications. A visualization tool was developed to explore the different facets and identify trends and gaps in the literature. The main findings are presented in terms of bibliometric analysis, and from the three perspectives considered. Various trends were derived from the database, among which we found that interactive sound installations are of prominent interest in the field of computer science. Furthermore, most installations described in the corpus consist of prototypes or belong to exhibitions, output two sensory modalities and include three or more sound sources. Beyond the trends, this review highlights a wide range of practices and a great variety of approaches to the design of interactive sound installations.