Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.
Arabic script is highly sensitive to changes in meaning with respect to the accurate arrangement of diacritics and other related symbols. The most sensitive Arabic text available online is the Digital Qur'an, the sacred book of Revelation in Islam that all Muslims including non-Arabs recite as part of their worship. Due to the different characteristics of the Arabic letters like diacritics (punctuation symbols), kashida (extended letters) and other symbols, it is written and available in different styles like Kufi, Naskh, Thuluth, Uthmani, etc. As social media has become part of our daily life, posting downloaded Qur'anic verses from the web is common. This leads to the problem of authenticating the selected Qur'anic passages available in different styles. This paper presents a residual approach for authenticating Uthmani and plain Qur'an verses using one common database. Residual (difference) is obtained by analyzing the differences between Uthmani and plain Quranic styles using XOR operation. Based on predefined data, the proposed approach converts Uthmani text into plain text. Furthermore, we propose to use the Tuned BM algorithm (BMT) exact pattern matching algorithm to verify the substituted Uthmani verse with a given database of plain Qur'anic style. Experimental results show that the proposed approach is useful and effective in authenticating multi-style texts of the Qur'an with 87.1% accuracy.
Translation equivalents are widely used in bilingual research concerning word processing (e.g., Eddington & Tokowicz, 2013; Jouravlev & Jared, 2020) and second-language vocabulary learning (e.g., Bracken et al., 2017; Degani et al., 2014). Although translation norms exist in several languages, to date there are no Malay-English translation norms. This study presents the first Malay-English translation norms collected with highly proficient Malay-English bilinguals. Furthermore, the study investigates the impact of lexical characteristics on translation ambiguity. The forward translation (FT) task (N = 30) collected English translations for 1004 Malay words selected from the Malay Lexicon Project (Yap et al., 2010), and subsequently the backward translation (BT) task (N = 30) gathered Malay translations for 845 English words obtained from the FT phase. The data revealed a high prevalence of translation ambiguity in both translation directions. Specifically, verbs, adjectives, and class-ambiguous words were more translation-ambiguous than nouns. Furthermore, within-language semantic variability and word length were positively correlated with translation ambiguity, whereas word frequency only correlated with translation ambiguity in FT. Word length and word frequency of the source words and their translations were positively correlated. Intriguingly, only in FT were bilinguals with higher Malay proficiency more likely to provide accurate and dominant translations for the Malay words. These findings contrast with those reported in translation norming studies involving other language pairs. The translation norms provide a useful resource for bilingual language studies involving Malay-English bilinguals.
Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60-86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain.
Previous research shows that people systematically match tastes with shapes. Here, we assess the extent to which matched taste and shape stimuli share a common semantic space and whether semantically congruent versus incongruent taste/shape associations can influence the speed with which people respond to both shapes and taste words. In Experiment 1, semantic differentiation was used to assess the semantic space of both taste words and shapes. The results suggest a common semantic space containing two principal components (seemingly, intensity and hedonics) and two principal clusters, one including round shapes and the taste word "sweet," and the other including angular shapes and the taste words "salty," "sour," and "bitter." The former cluster appears more positively-valenced whilst less potent than the latter. In Experiment 2, two speeded classification tasks assessed whether congruent versus incongruent mappings of stimuli and responses (e.g., sweet with round versus sweet with angular) would influence the speed of participants' responding, to both shapes and taste words. The results revealed an overall effect of congruence with congruent trials yielding faster responses than their incongruent counterparts. These results are consistent with previous evidence suggesting a close relation (or crossmodal correspondence) between tastes and shape curvature that may derive from common semantic coding, perhaps along the intensity and hedonic dimensions.
Resource bound security solutions have facilitated the mitigation of spatio-temporal attacks by altering protocol semantics to provide minimal security while maintaining an acceptable level of performance. The Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) routing protocol for Wireless Sensor Network (WSN) has been proposed to achieve a minimal selection of malicious nodes by introducing a dynamic collection window period to the protocol's semantics. However, its selection scheme suffers substantial packet losses due to the utilization of a single distance based parameter for node selection. In this paper, we propose a Fuzzy-based Geographic Forwarding protocol (FuGeF) to minimize packet loss, while maintaining performance. The FuGeF utilizes a new form of dynamism and introduces three selection parameters: remaining energy, connectivity cost, and progressive distance, as well as a Fuzzy Logic System (FLS) for node selection. These introduced mechanisms ensure the appropriate selection of a non-malicious node. Extensive simulation experiments have been conducted to evaluate the performance of the proposed FuGeF protocol as compared to DWSIGF variants. The simulation results show that the proposed FuGeF outperforms the two DWSIGF variants (DWSIGF-P and DWSIGF-R) in terms of packet delivery.
Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models.
Detecting semantic events in sports video is crucial for video indexing and retrieval. Most existing works have exclusively relied on video content features, namely, directly available and extractable data from the visual and/or aural channels. Sole reliance on such data however, can be problematic due to the high-level semantic nature of video and the difficulty to properly align detected events with their exact time of occurrences. This paper proposes a framework for soccer goal event detection through collaborative analysis of multimodal features. Unlike previous approaches, the visual and aural contents are not directly scrutinized. Instead, an external textual source (i.e., minute-by-minute reports from sports websites) is used to initially localize the event search space. This step is vital as the event search space can significantly be reduced. This also makes further visual and aural analysis more efficient since excessive and unnecessary non-eventful segments are discarded, culminating in the accurate identification of the actual goal event segment. Experiments conducted on thirteen soccer matches are very promising with high accuracy rates being reported.
Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.
In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.
Plectostoma is a micro land snail restricted to limestone outcrops in Southeast Asia. Plectostoma was previously classified as a subgenus of Opisthostoma because of the deviation from regular coiling in many species in both taxa. This paper is the first of a two-part revision of the genus Plectostoma, and includes all non-Borneo species. In the present paper, we examined 214 collection samples of 31 species, and obtained 62 references, 290 pictures, and 155 3D-models of 29 Plectostoma species and 51 COI sequences of 19 species. To work with such a variety of taxonomic data, and then to represent it in an integrated, scaleable and accessible manner, we adopted up-to-date cybertaxonomic tools. All the taxonomic information, such as references, classification, species descriptions, specimen images, genetic data, and distribution data, were tagged and linked with cyber tools and web servers (e.g. Lifedesks, Google Earth, and Barcoding of Life Database). We elevated Plectostoma from subgenus to genus level based on morphological, ecological and genetic evidence. We revised the existing 21 Plectostoma species and described 10 new species, namely, P. dindingensis sp. n., P. mengaburensis sp. n., P. whitteni sp. n., P. kayiani sp. n., P. davisoni sp. n., P. relauensis sp. n., P. kubuensis sp. n., P. tohchinyawi sp. n., P. tenggekensis sp. n., and P. ikanensis sp. n. All the synthesised, semantic-tagged, and linked taxonomic information is made freely and publicly available online.
In a meristic, morphometric and distributional study of Neolissochilus from Peninsular Malaysia, Khaironizam et al. (2015) subsumed Lissochilus tweediei Herre in Herre & Myers 1937 and a taxon they called "Tor soro Bishop 1973" into the synonymy of N. soroides (Duncker 1904) based on data collected from museum specimens. However, "Bishop 1973" is not the correct author citation for Tor soro. Instead, Tor (now placed in Neolissochilus) soro was originally described as Barbus soro by Valenciennes in Cuvier & Valenciennes (1842:191). Since "Tor soro Bishop 1973" is not a valid name/author combination, Neolissochilus soro, as treated by Khaironizam et al. (2015), cannot be considered a junior synonym of N. soroides.
The use of proverbs in Iban community illustrates that the community exercise language politeness in giving reproach, advice or insinuation to others. The elements used in the proverbs also reflect and describe the culture of this community by applying their experiences and observations to the environment. This study is conducted to analyze the natural elements in Ibans’ proverbs using inquisitive semantic analysis. This research is based on two objectives, namely listing and identifying Ibans’ proverbs (jaku' sempama) which contain elements of nature in the creation and to analyzing the mind and philosophy of Iban community in the creation of proverbs (jaku' sempama) by using inquisitive semantics as the theoretical framework. The natural elements identified are divided into 4 domains, namely animals, plants, resources or energy, and the universe. This is a qualitative research. Corpus data is gathered and two informants were interviewed. The meanings of the proverbs, the cognitive and the philosophy behind the creation of the proverbs plus the intellects of the Iban community are incorporated to reveal the Iban ingenuity behind the creation of a particular proverb. This study also incorporated knowledge from other areas in giving answers on the use of the natural elements identified in the Iban proverbs. Findings show that there are hidden philosophy behind the creations of the proverbs and this philosophy can be attributed to the culture of the community that behold the proverbs.
Catharanthus roseus (synonymous with Vinca rosea) is a perennial plant commonly seen in tropical countries. Seven species of this genus are native to Madagascar and one species is native to Southern Asia. It is more commonly known as Madagascar periwinkle. The local name in Malaysia is Kemunting Cina. The National Cancer Council of Malaysia (Majlis Kanser Nasional, MAKNA) uses the periwinkle logo as its symbol of hope for cancer patients. (Copied from article).
Life science ontologies play an important role in Semantic Web. Given the diversity in fish species and the associated wealth of information, it is imperative to develop an ontology capable of linking and integrating this information in an automated fashion. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information on unknown fish based on metadata restrictions. It is designed to support knowledge discovery, provide semantic annotation of fish and fisheries resources, data integration, and information retrieval. Automated classification for unknown specimens is a unique feature that currently does not appear to exist in other known ontologies. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1,830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users.
Previous experiments investigating visual search have shown that distractors that are semantically related to a search target can capture attention and slow the search process. In two experiments, we examine if distractors exactly matching, or semantically related to, search-irrelevant information held in working memory (WM) can also influence visual search while ruling out potential effects of color similarity. Participants first viewed and memorized an image of an everyday object, then they determined if a target item was present or absent in a two-object search array. On exact-match trials, the memorized object appeared as a distractor; on semantic-match trials, an object semantically related to the memorized object appeared as a distractor. Both exact-match and semantic-match distractors slowed search when the target was present in the search array. Our findings extend previous findings by demonstrating WM-driven attentional guidance by complex objects rather than simple features. The results also suggest that visual search can be influenced by distractors sharing only semantic features with a search-irrelevant, but active, WM representation.
INTRODUCTION: The Three-Factor Eating Questionnaire was first constructed to measure eating behavior in an English population in the United States. It has been validated and translated for various populations in different languages. The aim of this article is to describe a systematic process for translating the questionnaire from English to Malay language.
METHODOLOGY: The report of the International Society for Pharmacoeconomics and Outcome Research (ISPOR) Task Force was used as the basis for the systematic translation process. The process began with preparation; followed by forward translation (2 independent translators), reconciliation, back translation (2 independent translators), back translation review, harmonization, cognitive debriefing, review of cognitive debriefing results and finalization, proofreading; and ended with the final report. Four independent Malay translators who fluent in English and reside in Malaysia were involved in the process. A team of health care researchers had assisted the review of the new translated questionnaires.
RESULTS: Majority of the TFEQ-R21 items were experiencing, conceptually and semantically equivalence between original English and translated English. However, certain phrase such as "feels like bottomless pit" was difficult to translate by forward translators. Cognitive debriefing was a very helpful process to ensure the TFEQ-R21 Malay version was appropriate in term of wording and culturally accepted. A total of four redundant comments in regards to response scale wording, word confusion and wording arrangement.
CONCLUSION: The systematic translation process is a way to reduce the linguistic discrepancies between the English and Malay language in order to promote equivalence and culturally adapted TFEQ-R21 questionnaire.
A coding measure scheme numerically translates the DNA sequence to a time domain signal for protein coding regions identification. A number of coding measure schemes based on numerology, geometry, fixed mapping, statistical characteristics and chemical attributes of nucleotides have been proposed in recent decades. Such coding measure schemes lack the biologically meaningful aspects of nucleotide data and hence do not significantly discriminate coding regions from non-coding regions. This paper presents a novel fuzzy semantic similarity measure (FSSM) coding scheme centering on FSSM codons׳ clustering and genetic code context of nucleotides. Certain natural characteristics of nucleotides i.e. appearance as a unique combination of triplets, preserving special structure and occurrence, and ability to own and share density distributions in codons have been exploited in FSSM. The nucleotides׳ fuzzy behaviors, semantic similarities and defuzzification based on the center of gravity of nucleotides revealed a strong correlation between nucleotides in codons. The proposed FSSM coding scheme attains a significant enhancement in coding regions identification i.e. 36-133% as compared to other existing coding measure schemes tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.
Species of the Simulium (Simulium) melanopus species-group in Sabah are taxonomically revised by examining type specimens of S. (S.) crassimanum S. (S.) laterale, and S. (S.) nigripilosum, all described from females by Edwards in 1933, and newly collected samples from the vicinity of Mt. Kinabalu. The females of these three species are redescribed, and their males and pupae are described for the first time based on adults reared from pupae. Simulium (S.) liewi Takaoka, 2007 and S. (S.) kinabaluense Smart & Clifford, 1969 are synonymized with S. (S.) crassimanum and S. (S.) laterale, respectively. Simulium (S.) cheedhangi Takaoka, Sofian-Azirun & Ya'cob, 2015 is newly recorded from Sabah. Two new related species, S. (S.) lardizabalae and S. (S.) timpohonense, are described from males reared from pupae. Keys to identify eight species of the S. melanopus species-group in Sabah are provided for females, males, pupae and mature larvae.