Reviewerly/exHarmony huggingface dataset:
Image thresholding technique based on fractional derivative in fuzzy domain

A new image thresholding technique is proposed by combining fractional mask in fuzzy domain firstly fractional edge image is fuzzified. Furthermore, thresholds are identified for segmentation are evaluated using entropy, mean, median and maximum value of membership function. The experimental results demonstrated that the proposed method gives better results than Otsu, Kapur, Tsallis and Chand & Vese methods. The related simulations have been presented with MATLAB software.
Statistical Correction of the Distribution of Solar Radiation, Estimated by the Heliosat Method for Cuba

In this work, a statistical correction of the distribution of solar radiation in Cuba estimated by the Heliosat method is obtained, using images collected from the GOES-13 satellite for the period 2012&ndash;2017. A need has arisen for an improvement in the updating of the distribution of solar radiation in the country, because when the average annual maps of solar radiation estimated by the Heliosat method are compared with data from stations where solar radiation is measured, and when compared with the Annual Average Map of solar radiation published by the firm Solargis, an overestimation of solar radiation values was noted, as if the effect of cloud cover attenuation, as calculated by Heliosat, was less than the effect of real cloud attenuation. Therefore, it is necessary to improve the updating of the behavior of solar radiation in Cuba due to the relevance of this information related to solar radiation as a renewable source of energy, and due to its use in the evaluation of the country&rsquo;s helioenergetic potential.
Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things

An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various applications. A few examples of such problems are alignment and assembly of short reads from next generation sequencing (NGS), comparing DNA sequences, and determining the frequency of a pattern in a sequence. The approximate matching of DNA sequences is also well suited for many applications equivalent to the exact matching of the sequence since the DNA sequences are often subject to mutation. Consequently, recognizing pattern similarity becomes necessary. Furthermore, it can also be used in virtually every application that calls for pattern matching, for example, spell-checking, spam filtering, and search engines. According to the traditional approach, finding a similar pattern in the case where the sequence length is ls and the pattern length is lp occurs in O (ls∗lp ). This heavy processing is caused by comparing every character of the sequence repeatedly with the pattern. The research intended to reduce the time complexity of the pattern matching by introducing an approach named "optimized pattern similarity identification" (OPSI). This methodology constructs a table, entitled "shift beyond for avoiding redundant comparison" (SBARC), to bypass the characters in the texts that are already compared with the pattern. The table pertains to the information about the character distance to be skipped in the matching. OPSI discovers at most spots of similar patterns occur in the sequence (by ignoring è mismatches). The experiment resulted in the time complexity identified as O (ls . è). In comparison to the size of the pattern, the allowed number of mismatches will be much smaller. Aspects such as scalability, generalizability, and performance of the OPSI algorithm are discussed. In comparison with the hamming distance-based approximate pattern matching algorithm, the proposed algorithm is found to be 69% more efficient.
A Subspace Fusion of Hyper-parameter Optimization Method Based on Mean Regression

For object detection methods based on neural networks in computer vision, hyper-parameter is a crucial factor in the detection performance. Traditional hyperparameter optimization methods share the following shortcomings. (1) Search performance depends heavily on historical data and computational resources. (2) The open-loop structure of models may lead to unstable search results. We take missed detection targets as feedback to establish an iterative search model and propose a subspace-fusion optimization method based on mean regression. Firstly, the Successive Halving algorithm is deployed to determine the initial seeds, then detection subspaces and missed detection subspaces are generated according to the object detection results, and anchor-vector-based mean regressions are performed in the two subspaces respectively. Finally, we obtain the optimal parameters by a linear fusion of the two regression results. An early termination strategy is embedded into the search process to stop the invalid searches. Experiments show that within limited resource, this paper achieves significant improvement in search efficiency and detection performance compared with the classical methods.
Water Gauge Image Augmentation based on Generative Adversarial Network

Water level monitoring based on water gauge is a very widely used way because of its cheapness and portability. However, the insufficiency and low quality of water gauge images restrict the performance of water level measuring task based on deep learning methods such as object detection and semantic segmentation. In this article, we proposed a generative adversarial network (GAN) named Contextual adjustment GAN (CA-GAN) for data augmentation of water gauge images obtained from Wuyuan, Jiangxi Province in China, i.e. CA-GAN can generate high-quality images containing various scales and types water gauge, which provide image data for application such as deep-learning based water level measuring method. First, a improved downsampling module is designed with the help of segmentation map for the semantic activation modulation. Then, the Unet++ structure with the improved downsampling module is applied in the generator. Finally, to modulate the semantic relationship, a contextual adjustment scheme is de-signed between adjacent layers. This article conducts detailed experiments, proving that the improved downsampling module contributes to the maintenance of semantic information of water gauge images. It is illustrated that the water gauge images generated by CA-GAN have higher quality comparing with other three GAN models. And our method is expected to promote the water level measurement and hydrological monitoring application development.
A hybrid SOM and HMM classifier in a Fog Computing gateway for Ambient Assisted Living Environment

Implementing Machine Learning algorithms with low-power devices and limited computational resources is challenging. Research on temporal data of Ambient Assisted Living (AAL) environment sensors to handle many states of the deep learning models that are used to train unsupervised data is limited. Computational aspects of the data training and inferring the insights from the sensor data of the AAL environment are essential aspects of a fog computing framework. The AAL environment embodies a fog computing structure with limited computing capabilities. This paper studies how to train and infer meaning information from the AAL sensor data using a hybrid algorithm of Self Organizing Map (SOM) and Hidden Markov Model (HMM) on a resource constraint computing device such as Raspberry Pi was explored. The research investigations reveal that the execution of the hybrid method on the fog computing gateway could cluster the anomalous instances accurately.
An Algorithm with Smooth Filtering Based on LPC

Many professions rely on disguised voice techniques. In contrast to the conventional linear prediction-based modification technique, we propose a disguised voice algorithm with smoothing filtering to improve the modification effect by smoothing the changed pitch period. This paper is based on linear prediction. The cepstrum method is used to obtain and change the pitch period of the speech residual signal, applying the smoothing process on the obtained pitch period, and the speech synthesis is realized using a linear prediction synthesis filter to achieve the pitch change with the same speech rate. The results show that the synthesized signal tends to be more clear and natural and the modification effect is better than no smoothing.
Application of GIS Technology-Supported Cross Media Fusion Method Based on Deep Learning in Landscape Performance Evaluation

GIS technology can provide reasonable and sustainable data support for landscape planning and ecological development and make wetland landscape planning consider the spatial layout of landscape and the optimal allocation of resources more. The key technologies of cross media intelligence mainly focus on intelligent information retrieval, analysis and reasoning, knowledge map construction, and intelligent storage. Convolutional neural network (CNN), as one of the representative algorithms of deep learning, plays an important role in retrieving landscape data and extracting image and text features across media. Further retrieval of media data, in-depth text processing, and image feature data extraction are realized by using deep learning technology, and comprehensive in-depth analysis is carried out by combining landscape plane images, three-dimensional images, and vector information in GIS technology. Provide quantitative information for the evaluation system of human landscape, economy, history, and region, so as to formulate a scientific and reasonable performance evaluation system.
Develop corpora and methods for cross-lingual text reuse detection for English Urdu language pair at lexical, syntactical, and phrasal levels

In recent years, Cross-Lingual Text Reuse Detection (CLTRD) has attracted the attention of the research community because large digital repositories and efficient Machine Translation systems are readily and freely available, which makes it easier to reuse text across the languages and very difficult to detect it. In the previous studies, the problem of CLTRD for the English-Urdu language pair has been explored at the sentence/passage and document level, and benchmark corpora and methods have been developed. However, there is a lack of benchmark corpora and methods for the CLTRD for the English-Urdu language pair at the lexical, syntactical, and phrasal levels. To fulfill this research gap, this study presents three large benchmark corpora for detecting the Cross-Lingual Text Reuse (CLTR) at three levels of rewrite (Wholly Derived (WD), Partially Derived (PD), and Non Derived (ND)). The CLEU-Lex, CLEU-Syn and CLEU-Phr corpora contain 66,485 (WD = 22,236, PD = 20,315 and ND = 23,934), 60,267 (WD = 20,007, PD = 16,979 and ND = 23,281) and 60,106 (WD = 23,862, PD = 15,878 and ND = 20,366) CLTR pairs respectively. As a secondary major contribution, we have applied the Cross-Lingual Word Embedding (CLWE), Cross-Lingual Semantic Tagger (CLST), and Cross-Lingual Sentence Transformer (CLSTR) based methods on our three proposed corpora for the CLTRD. Our extensive experimentation showed that for the binary classification task, the best results on the CLEU-Lex corpus were obtained using the cross-lingual sentence transformer ( $$F_{1}$$ = 0.80). For the CLEU-Syn and CLEU-Phr corpora, the best results were obtained using the cross-lingual sentence transformer and a combination of the CLWE, CLST and CLSTR methods ( $$F_{1}$$ = 0.92 on CLEU-Syn and $$F_{1}$$ = 0.94 on CLEU-Phr). For the ternary classification task, the best results on the CLEU-Lex corpus were obtained using the cross-lingual sentence transformer method ( $$F_{1}$$ = 0.69). For the CLEU-Syn corpus, the best results were obtained using a combination of the CLWE, CLST, and CLSTR methods ( $$F_{1}$$ = 0.82). For the CLEU-Phr corpus the best results were obtained using cross-lingual sentence transformer and combination of CLWE, CLST, and CLSTR methods ( $$F_{1}$$ = 0.78). To foster and promote research in Urdu (a low-resourced language) all the three proposed corpora are free and publicly available for research purposes.
An Efficient ‘Paper Mining’ System to Search Academic Papers Using SPECTER Model

In this research, we have developed an academic paper search application "Paper Mining" that can search related papers from Title and Abstract using the SPECTER model, which is one of the natural language processing (NLP). Paper Mining can efficiently and effectively search related articles comprehensively even for beginners who do not know the appropriate keywords. In order to confirm whether the SPECTER model installed in Paper Mining is actually effective in arXiv's article search, we examined the evaluation method and confirmed the search accuracy. This research, which introduced the SPECTER model into a web application, will contribute as a precedent example of future model utilization.
How Populism Harms Prosperity: Unified Populist Rule Reduces Investment, Innovation, and Productivity

The economic cost of populism is high. This paper finds that when a populist executive rules alongside a populist majority in the legislature, this reduces a country’s growth rate of real Per Capita Income by 3.7% below its trend, a phenomenon that spans over a 12-year period before returning to the pre-populist equilibrium. We also outline the mechanics behind these numbers, and provide systematic evidence for our argument. Populists from either the left or right, in rich or poor countries, in nations open to trade or more autarkic ones, and across both liberal democracies and authoritarian regimes, tend to converge on a similar political economic model: protectionism, crony capitalism, and redistribution. This undermines productivity by increasing the size of government without parallel investments in infrastructure, basic science, and education, and by reducing private investment, R&D spending, researchers per capita, and patents per capita. We arrive at these findings by estimating ARDL dynamic panel models via System GMM, which increases our confidence that these long run effects run from populism to economic underdevelopment. To do so, we introduce a new measure of populism that identifies whether populists control both the executive and legislative branches and illustrate how Argentina’s post World War II political and economic history elucidates our framework.
Geoderma Organic Geochemical Biomarkers for Estimation of Petroleum Inputs in Soil of Alluvial Area Near Sava River, New Belgrade (Serbia)

Abstract : Petroleum pollution of soil is spread all over the world, and present potential risk for grade of whole environment. The various sources of this pollution, such as accidental spillage of petroleum and its derivatives, industrial emission, can cause major consequences for entire ecosystems. In this study, soil samples were collected from surface layers with depth of 0.0 - 0.3 m, in the vicinity of the heating plant, which is located in the alluvial area of the Sava River, at New Belgrade (Serbia). The content of the extracted organic substance were gravimetrically determined, and it was in range 102.59 - 1428.57 mg / kg. This was the first step that is confirmed potential petroleum pollution of the investigated area. Qualitative and quantitative data were obtained by gas chromatograph connected with a with a flame ionization detector (GC-FID) and by GC linked to a mass spectrometer (GC/MS). These analyses were shown a presence of petroleum pollution based on the presence of n -alkanes, as one of the biomarkers of petroleum pollution, which concentrations was in range from 3.25 to 35.24 mg / kg. The results also showed that the top soil contained higher concentration of TPH (90.65 mg kg −1 ). This may be a result was caused by crude oil spills and leaks which continuously occured for long periods of time. Combination of different evaluation indices (CPI, ACL, LWM/HWM, UCM, U/R, Pr/Phy, n -C 17 /Pr, n -C 18 /Phy) were also calculated in order to determine anthropogenic input of hydrocarbons in the analysed soil samples. The results from spatial PCA FA suggested that most of the variations could be explained by a set of natural and anthropogenic pollutions. Detailed risk assessment could help to take necessary actions to protect public health in this area.
Using Contrastive Inferences to Learn About New Words and Categories

In the face of unfamiliar language or objects, description is one cue people can use to learn about both. Beyond narrowing potential referents to those that match a descriptor (e.g., "tall"), people could infer that a described object is one that contrasts with other relevant objects of the same type (e.g., "the tall cup" contrasts with another, shorter cup). This contrast may be in relation to other objects present in the environment (this cup is tall among present cups) or to the referent's category (this cup is tall for a cup in general). In three experiments, we investigate whether people use such contrastive inferences from description to learn new word-referent mappings and learn about new categories' feature distributions. People use contrastive inferences to guide their referent choice, though size--and not color--adjectives prompt them to consistently choose the contrastive target over alternatives (Experiment 1). People also use color and size description to infer that a novel object is atypical of its category (Experiments 2 and 3). However, these two inferences do not trade off substantially: people infer a described referent is atypical even when the descriptor was necessary to establish reference. We model these experiments in the Rational Speech Act (RSA) framework and find that it predicts both of these inferences, and a very small trade-off between them--consistent with the non-significant trade-off we observe in people's inferences. Overall, people are able to use contrastive inferences from description to resolve reference and make inferences about a novel object’s category, allowing them to learn more about new things than literal meaning alone allows.
B-AT-KD: Binary attention map knowledge distillation

Convolutional neural networks (CNNs) have been extensively used in a number of applications and have shown to be quite effective. As the depth and width of the network expand, not only does prediction accuracy improve, but so does the network’s training complexity. To solve this problem, the knowledge distillation (KD) network, which consists of a teacher and a student network, is established. Furthermore, the widely accessible attention mechanism is important in the KD. Therefore, we build the Binary Attention Map Knowledge Distillation (B-AT-KD) model to enhance the performance of the student network by combining the output of the teacher network and associated attention maps with distinct semantics to supervise the student network. In addition, to improve training results, we propose a new global loss function called KDM-Loss and use hyperparameters search to assign suitable weights. Finally, we compare our B-AT-KD to state-of-the-art KD methods on the CIFAR 10, CIFAR 100, and Mini-ImageNet datasets. Experiments reveal that our proposed approach improves CIFAR 10 precision by 3.98%, Mini-ImageNet accuracy by 2.30%, and CIFAR 100 precision by 2.51% while reducing the number of parameters and computations by more than 50%.
Cross-Modal Discrimination Hashing Retrieval Using Variable Length

Fast cross-modal retrieval technology based on hash coding has become a hot topic for the rich multimodal data (text, image, audio, etc.), especially security and privacy challenges in the Internet of Things and mobile edge computing. However, most methods based on hash coding are only mapped to the common hash coding space, and it relaxes the two value constraints of hash coding. Therefore, the learning of the multimodal hash coding may not be sufficient and effective to express the original multimodal data and cause the hash encoding category to be less discriminatory. For the sake of solving these problems, this paper proposes a method of mapping each modal data to the optimal length of hash coding space, respectively, and then the hash encoding of each modal data is solved by the discrete cross-modal hash algorithm of two value constraints. Finally, the similarity of multimodal data is compared in the potential space. The experimental results of the cross-model retrieval based on variable hash coding are better than that of the relative comparison methods in the WIKI data set, NUS-WIDE data set, as well as MIRFlickr data set, and the method we proposed is proved to be feasible and effective.
Journalists and Freedom of Speech in Nigeria: Reality or Imagination?

Press Freedom of Speech has become one of the major challenges facing Journalism practice in the world. This paper explores the factors hindering Press Freedom of Speech in Nigeria using some relevant examples. The researcher used the qualitative method of research; using secondary data comprising of books and journals. The study is embedded on social responsibility theory. The study reveals that there are a lot of factors in Nigeria that hinders Press Freedom of Speech ranging from secrecy, legal pressure, direct censorship and force among others. Findings also reveal that Nigerian Press Freedom of Speech is a paradox and only exist on paper i.e. in the constitution but not in practice. The study recommends that since Press Freedom of Speech is granted in the constitution of Nigeria, there should be statutory backup and in order not to hinder Press Freedom of Speech, journalist should be allowed to have access to government sources and records so long as it will not bring chaos to the society at large.
Real-Time Video Anomaly Detection for Smart Surveillance

Human monitoring of surveillance cameras for anomaly detection may be exhaustive and monotonous task as it requires constant attention to judge if the captured activities are anomalous or suspicious. Hence, this paper presents a real-time expert system based on computer vision technology and deep learning models for autonomous video anomaly detection (AVAD). First, background subtraction (BS) was performed on each frame by modeling each pixel as a mixture of Gaussians (MoG) to concatenate only the higher order learning in the foreground. Next, deep learning techniques are customized to filter out unnormal events from normal ones and automatically identify signs of threat and violence in real-time. The vision-based system employs autoencoders for their ability to work with little to no supervision. Specifically, convolutional autoencoders are used as semi-supervised learning of convolution filters to minimize reconstruction errors by learning the optimal filters. Then, object detection was introduced to minimize human intervention in video stream processing. At recognition time, the network generates an alarm for the presence of an anomaly to notify of the identification of a potentially dangerous scenario, showing in a time instance the suspicious actions. Finally, the complete system is validated upon several benchmark datasets and proven to be robust for complex video anomaly detection. The average Area Under the Curve (AUC) value of the frame-level evaluation for all benchmarks is 94.94%. The best improvement ratio of AUC between AVAD and state-of-the-art methods is 7.7%.