Reviewerly/exHarmony huggingface dataset:
Sorting of Objects Using Image Processing

Sorting is a regular process for many industries and small businesses. The paper proposes a low-cost automated system using Deep Learning, Raspberry Pi 3 module B, and a conveyor belt system for sorting of various day to day products. Object sorting is done based on their features. The physical implementation includes a conveyor belt and a pneumatic piston, which is controlled by the Raspberry Pi for the sorting of the object. TensorFlow, with some libraries and methods, is used for the detection of the object.
Novel textual entailment technique for the Arabic language using genetic algorithm

This paper presents a textual entailment (TE) model that considers entailment as an optimization problem. The proposed TE model employs a genetic algorithm to derive an optimal similarity function and correlated entailment judgment threshold. The similarity function is formulated through a linear combination of text similarity measures and weights. Two text similarity measures are considered: cosine and the longest common substring. These text similarity measures are computed for each text pair. The weights represent the importance of the considered text similarity measures for generating an entailment judgment. The weights and correlated judgment thresholds are obtained by the genetic algorithm. Several experiments are conducted using the ArbTED dataset to evaluate the performance of the proposed model. Comparative results demonstrate the superiority of the proposed model. On average, the model achieved a 16% improvement in terms of accuracy. Furthermore, the average recall and precision values were 72.7% and 72.3%, respectively.
Sentiment analysis towards Jokowi`s government using twitter data with convolutional neural network method

Abstract In this current digital era, internet has become commonly uses in the society, it become an important aspect in life, one of its function are as a communication platform and channel for public opinion. In which utilize social media platforms. Indonesia really active in communication and conveyor expression using social media. In such a way that indonesia’s government takes advantage of it as a way to communicate and get feedback from indonesian people. In this research, as jokowi lead his second periode as a president of indonesian’s government, we try to see how indonesian sentiment toward his leadership. Using Convolutional Neural Network method from 7228 row data crawled from twitter obtained 38% positive sentiment, 51% negative sentiment, and 18% neutral sentiment. Overall we have 57% prediction accuracy using CNN model.
A Survey of Multi-Ethnic Face Feature Recognition

At present, face recognition technology is widely used in various fields, which effectively promotes the process of information automation and data fusion. Face recognition technology is based on in-depth analysis of image processing technology and high-precision control of face dynamic changes. Identification function. To fully expand the application fields of face recognition technology, according to the humanistic depth and regional environmental characteristics in a certain period, this article will form a phased research report on the development of face recognition technology on this basis. The progress of face classification research in various countries. The report mainly analyzes the domestic situation. Summarized the current status of foreign research, analyzed the differences in facial features in some countries, and analyzed the current common facial feature extraction methods and the advantages and disadvantages of several research methods.
Analyzing the quality of local and global multidimensional projections using performance evaluation planning

Among the challenges of the big data era, the analysis of high-dimensional data is still an open research area. As a result, several multidimensional projection techniques have been developed to reduce data dimensionality, becoming important visualization and visual analytics tools. In order to ensure the quality of projections, it is necessary to assess the low-dimensional embeddings by using different dataset configurations as input and analyzing evaluation metrics. However, it is not clear to the user how factors such as the number of dimensions, instances, or clusters, can affect the projection mapping and its quality regarding different projection techniques and assessment metrics. The research reported in this paper aims to clarify how much these factors affect each response variable via performance evaluation planning. We present an evaluation approach, supported by factorial design, that carries out a complete analysis, in the sense of measuring all possible combinations of all the input factors. The results of the analyses of local and global structure preservation in the projections yield a better understanding of how distinct dataset properties can influence the choice of projections based on quality metrics results.
Semi-Supervised Landmark-Guided Restoration of Atmospheric Turbulent Images

Image degradation due to atmospheric turbulence (AT), which is common while capturing images at long ranges, adversely affects the performance of tasks such as face alignment and face recognition. To the best of our knowledge, there does not exist any dataset consisting of turbulence-degraded face images along with their annotated landmarks and ground-truth clean images, making supervised training challenging. In this paper, we present a semisupervised method for jointly extracting facial landmarks and restoring the degraded images by exploiting the semantic information from the landmarks. The proposed approach learns to generate AT images by combining the content from a clean image and turbulence information from AT images in an unpaired manner. Next, we use heatmaps from the landmark localization network as a prior to the image restoration module. Subsequently, we impose heatmap consistency loss and heatmap confidence loss to regularize the restored images. Extensive experiments demonstrate the effectiveness of the proposed network, which achieves an NME of 2.797 on the task of landmark localization for strong turbulent images and yields improved restoration results compared to state-of-the-art methods.
The effectiveness of communication and online language disruption during the era of pandemic covid-19 in senior high school students in implementation of learning cycle 7e

Abstract OECD in Paris reveals the result of Programme for International Assessment (PISA) 2018 that the ability of students’ literacy is 371 out of 487, which means that there are 30% of Indonesian students who have minimum ability of reading. The things that have relation to students’ ability in reading are communication and language disruption. During pandemic of COVID-19, learning activity is conducted by using online, in which there are some obstacles and problems in showing the material through visual and audio that create miscommunication among the teachers and the students. In this case, learning cycle 7E is one of learning that is centred in students by emphasizing on the effectiveness of communication and language disruption. The phases of learning cycle 7E are: Elicit, Engage, Explore, Explain, Elaborate, Evaluate, and Extend. This research is conducted by using descriptive qualitative. 32 students of SMA IT Misykat Al Anwar are purposively chosen as the research subject. The research instruments involved are observation, teaching scenario, and interview. It is analysed by using data reduction, data analysis, and conclusion. The result of this research that is conducted in 4 meetings reveals that the students can communicate systematically in language structure implication and the level of language content legibility are fulfilled, moreover, students’ verbal communication can expose the students’ schemata which can less its miscommunication, other than that, students’ language disruption during the implementation of Learning Cycle 7E shows that the students are more innovative in expressing the language content in implementing the digital technology, therefore, it is way more efficient and good result. Therefore, it is needed to apply the Learning Cycle 7E in the era of pandemic Covid-19 and answers the challenge of industry 4.0 Era
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance

Co-predication is one of the most frequently used linguistic tests to tell apart shifts in polysemic sense from changes in homonymic meaning. It is increasingly coming under criticism as evidence is accumulating that it tends to mis-classify specific cases of polysemic sense alteration as homonymy. In this paper, we collect empirical data to investigate these accusations. We asses how co-predication acceptability relates to explicit ratings of polyseme word sense similarity, and how well either measure can be predicted through the distance between target words’ contextualised word embeddings. We find that sense similarity appears to be a major contributor in determining co-predication acceptability, but that co-predication judgements tend to rate especially less similar sense interpretations equally as unacceptable as homonym pairs, effectively mis-classifying these instances. The tested contextualised word embeddings fail to predict word sense similarity consistently, but the similarities between BERT embeddings show a significant correlation with co-predication ratings. We take this finding as evidence that BERT embeddings might be better representations of context than encodings of word meaning.
Comparison of Various Word Embeddings for Hate-Speech Detection

Word Embedding plays a crucial role in natural language processing, and other related domains. The vast variety of language modelling and feature learning techniques often concludes in a quandary. The motivation behind this work was to produce comparative analysis among these methods and finally use them to flag hate-speech on social media. The progress in these word embedding techniques has led to remarkable results by incorporating various natural language applications. Understanding the different context of polysemous words is one of the features that evolved over time with these word embedding models. A systematic review on varying word embedding methodologies has been performed in this paper. Various experimental metrics have been used and detailed analysis has been done on each word embedding model. It is shown that analysis involves various aspects of the model like dealing with multi-sense words, and rarely occurring words, etc., and finally a coherent analysis report is presented. The various models under analysis are—Word2Vec (Skip-Gram, CBOW), GloVe, Fast-Text and ELMo. These models are then put to a real-life application in the form of Hate Speech detection of twitter data, and their individual capacities and accuracies are compared. Through this paper we show how ELMo uses different word embeddings for polysemous words to capture the context. We show how Hate speech can be better detected by ELMo because such speech requires better understanding of context of words for segregation from normal speech/text.
Cloud Service Provider Selection Using Fuzzy TOPSIS

Cloud Providers allows on-demand access and simple network connections to a shared resource pool. Because of the success and benefits of the use of Cloud Services, a range of business organizations are heading towards Cloud. The selection of the best Cloud Provider is a research topic, and this article addressing the same. Several other methods for addressing this problem and researchers suggested deploying Multi Criteria Decision-Making Models. Nevertheless, a significant number of works focused on quantitative QoS attributes and also the fuzziness involved in the QoS data was not handled in the existing works. So a framework for ranking and selection of Cloud Providers is proposed in this work. SMI attributes that are proposed by CSMIC and approved by ISO is used as the criteria for the ranking of the cloud providers in this research work. Fuzzy TOPSIS is the MCDM approach used to rank and select the best Cloud Provider in this proposed work.
Automated model extraction: From non-deterministic C code to active objects

The C programming language is well-known to have a large amount of underspecified behavior that often results in non-determinism even of sequential programs. In many application areas, not necessarily safety-critical ones, this is highly undesirable. A number of approaches and tools that statically analyze such behavior have been suggested, but they suffer from a high number of false positives and negatives. We present a novel model-based approach to analyzing non-determinism that works by automatic extraction of a faithful model of a given C program in a concurrent active object language. The extracted model renders any non-deterministic behavior of the C program in terms of explicit concurrency. This opens the door to global, semantic analyses. We give a fully formal account of the model extraction process and present an experimental evaluation of its implementation in the model extraction tool C2ABS.
RETRACTED: Estimating the Imaging in Medical Science Using Image Processing Techniques

Abstract Automatic image search is proving prominent in the medical imaging domain because it includes commands such as division, extraction of vital elements of the agent, coordination, layout, monitoring, the examination of movement, estimation of defacement, and three-dimensional recreation. The character of the input images takes on a polar job within the scope of any image review task. The higher image quality produces simplified and less advanced procedures for image processing. Consequently, inexpensive processes such as clamping, geometric adjustment, edge enhancement, differentiation, or brightness review will be required for the image processing. Despite the difficulties, automatic techniques for processing and searching for images are applicable for processing and searching for images is applicable for a good range of uses. In this paper, there are techniques we have appraised to process and examine objects in the images. In the following sections, these techniques will be introduced in applications from drugs and biomechanics to development and material sciences.
Effective Strategies for Low-Resource Reading Comprehension

Machine reading comprehension (MRC) has recently reached human-level accuracy on resource-rich languages (e.g. English). However, for low-resource MRC, there is a huge gap between machine and human performance due to limited annotated data. To narrow this gap, we investigate three strategies, namely data augmentation via translation, multilingual training and cross-lingual fine-tuning, to improve low-resource MRC via knowledge transfer. Experiments on a small Chinese MRC dataset (CMRC2018), demonstrate that our strategies are capable of leveraging knowledge from the English SQuAD dataset. Furthermore, the combination of the three strategies achieves significant improvements on the DRCD (the Delta Reading Comprehension Dataset).
CBAC4C: conflict‐based VM isolation control for cloud computing

Abstract For businesses to benefit from the many opportunities of cloud computing, they must first address a number of security challenges, such as the potential leakage of confidential data to unintended third parties. An inter‐VM (where VM is virtual machine) attack, also known as cross‐VM attack, is one threat through which cloud‐hosted confidential data could be leaked to unintended third parties. An inter‐VM attack exploits vulnerabilities between co‐resident guest VMs that share the same cloud infrastructure. In an attempt to stop such an attack, this paper uses the principles of logical analysis to model a solution that provides physical separation of VMs belonging to conflicting tenants based on their levels of conflict. The derived mathematical model is founded on scientific principles and implemented using four conflict‐aware VM placement algorithms. The resultant algorithms consider a tenant's risk appetite and cost implications. The model offers guidance to VM placement and is validated using a proof of concept. A cloud simulation tool was used to test and evaluate the effectiveness and efficiency of the model. The findings reflect that the introduction of the proposed model introduced a time lag in the time it took to place VM instances. On top of this, it was also discovered that the number and size of the VM instances has an effect on the VM placement performance. The findings further illustrate that the conflict tolerance level of a VM has a direct impact on the time it took to place.
Adaptive kernel scaling support vector machine with application to a prostate cancer image study

The support vector machine (SVM) is a popularly used classifier in applications such as pattern recognition, texture mining and image retrieval owing to its flexibility and interpretability. However, its performance deteriorates when the response classes are imbalanced. To enhance the performance of the support vector machine classifier in the imbalanced cases we investigate a new two stage method by adaptively scaling the kernel function. Based on the information obtained from the standard SVM in the first stage, we conformally rescale the kernel function in a data adaptive fashion in the second stage so that the separation between two classes can be effectively enlarged with incorporation of observation imbalance. The proposed method takes into account the location of the support vectors in the feature space, therefore is especially appealing when the response classes are imbalanced. The resulting algorithm can efficiently improve the classification accuracy, which is confirmed by intensive numerical studies as well as a real prostate cancer imaging data application.
Next-POI Recommendations Matching User’s Visit Behaviour

Abstract We consider the urban tourism scenario, which is characterized by limited availability of information about individuals’ past behaviour. Our system goal is to identify relevant next Points of Interest (POIs) recommendations. We propose a technique that addresses the domain requirements by using clusters of users’ visits trajectories that show similar visit behaviour. Previous analysis clustered visit trajectories by aggregating trajectories that contain similar POIs. We compare our approach with a next-item recommendation state-of-the-art Neighbour-based model. The results show that customizing recommendations for clusters of users’ with similar behaviour yields superior performance on different quality dimensions of the recommendation.
Energy Yield and Clipping Loss Corrections for Hourly Inputs in Climates with Solar Variability

High-frequency measurements of solar resource from the National Institute of Standards and Technology (NIST) PV test-bed in Gaithersburg, MD were down-sampled from 1-minute to 1-hour and used to predict energy yield and clipping losses. Three virtual systems based on the NIST ground array with increasing DC/AC ratio were compared. With hourly input, clipping losses were smaller and energy yield was larger than predictions with high-frequency input. Systems with higher DC/AC ratio were more sensitive to time-resolution. The average difference between hourly and 1-minute predicted energy yield was 2% during the month of July for a DC/AC ratio of 1.3. These results demonstrate the importance of high-frequency solar resource in climates with solar variability to avoid over-predictions of energy yield for inverter blocks with a DC/AC ratio greater than one. The results of this study were implemented to develop a sub-hourly clipping adjustment factor for a pre-construction project site codenamed, “Orion,” near Northbridge, Massachusetts.
Sensitivity Analysis of Photovoltaic System Design Parameters to Passively Mitigate Ramp Rates

As solar photovoltaic (PV) generation continues to become a larger part of the energy mix, the challenges associated with their inherent resource variability need to be addressed. Approaches often considered for limiting variability are enhanced cloud forecasting and the utilization of energy storage systems. In this article, we explore addressing variability through the inherent design of the solar site, avoiding the use of additional equipment such as cloud cameras or energy storage. Additionally, we evaluate the sensitivity that three key design parameters have on a PV site's ramp rate, and how intelligent design can limit ramping. A PV power model was developed using plane of array irradiance calculations and manufacturer hardware parameters. Using high resolution environmental data from a site with a relatively large quantity of ramping, annual energy generation and ramp rate estimates for a baseline PV site are estimated as each design parameter is swept. The results show that varying design parameters from a typical “optimal” energy configuration can passively reduce ramp rates by almost 50% with the energy and financial impact also evaluated.
Resting-state functional connectivity predicts recovery from visually induced motion sickness

Movies depicting certain types of motion often provoke uncomfortable symptoms similar to motion sickness, termed visually induced motion sickness (VIMS). VIMS generally evolves slowly during the viewing of a motion stimulus and, when the stimulus is removed, the recovery proceeds over time. Recent human neuroimaging studies have provided new insights into the neural bases of the evolution of VIMS. In contrast, no study has investigated the neural correlates of the recovery from VIMS. Study of the recovery process is critical for the development of a way to promote recovery and could provide further clues for understanding the mechanisms of VIMS. We thus investigated brain activity during the recovery from VIMS with functional connectivity magnetic resonance imaging. We found enhanced recovery-related functional connectivity patterns involving brain areas such as the insular, cingulate and visual cortical regions, which have been suggested to play important roles in the emergence of VIMS. These regions also constituted large interactive networks. Furthermore, the increase in functional connectivity was correlated with the subjective awareness of recovery for the following five pairs of brain regions: insula–superior temporal gyrus, claustrum–left and right inferior parietal lobules, claustrum–superior temporal gyrus and superior frontal gyrus–lentiform nucleus. Considering the previous findings on the functions of these regions and the present results, it is suggested that the increase in FC may reflect brain processes such as enhanced interoceptive awareness to one’s own bodily state, a neuroplastic change in visual-processing circuits and/or the maintenance of visual spatial memory.
The Wolf pack assignment rule based on ant colony algorithm and the path planning of scout ants in complex raster diagra

Abstract The traditional ant colony algorithm based on raster algorithm has slow searching speed and convergence speed in the problem of robot path planning, and it is easy to fall into the problem of local optimal. In order to solve this problem, an improved ant colony algorithm based on Wolf distribution rules and reconnaissance ant strategy is proposed, which can adjust the pheromone coefficient adaptively and obtain a more suitable proportion of pheromone allocation. Experiments show that this algorithm is superior to the traditional ant colony algorithm in terms of optimal path searching speed, quality and convergence speed in simple and complex environments.