Futuristic Prediction of Missing Value Imputation Methods Using Extended ANN

Missing data is universal complexity for most part of the research fields which introduces the part of uncertainty into data analysis. We can take place due to many types of motives such as samples mishandling, unable to collect an observation, measurement errors, aberrant value deleted, or merely be short of study. The nourishment area is not an exemption to the difficulty of data missing. Most frequently, this difficulty is determined by manipulative means or medians from the existing datasets which need improvements. The paper proposed hybrid schemes of MICE and ANN known as extended ANN to search and analyze the missing values and perform imputations in the given dataset. The proposed mechanism is efficiently able to analyze the blank entries and fill them with proper examining their neighboring records in order to improve the accuracy of the dataset. In order to validate the proposed scheme, the extended ANN is further compared against various recent algorithms or mechanisms to analyze the efficiency as well as the accuracy of the results.

Machine Learning and Soil Humidity Sensing: Signal Strength Approach

The Internet-of-Things vision of ubiquitous and pervasive computing gives rise to future smart irrigation systems comprising the physical and digital worlds. A smart irrigation ecosystem combined with Machine Learning can provide solutions that successfully solve the soil humidity sensing task in order to ensure optimal water usage. Existing solutions are based on data received from the power hungry/expensive sensors that are transmitting the sensed data over the wireless channel. Over time, the systems become difficult to maintain, especially in remote areas due to the battery replacement issues with a large number of devices. Therefore, a novel solution must provide an alternative, cost- and energy-effective device that has unique advantage over the existing solutions. This work explores the concept of a novel, low-power, LoRa-based, cost-effective system that achieves humidity sensing using Deep Learning techniques that can be employed to sense soil humidity with high accuracy simply by measuring the signal strength of the given underground beacon device.

Embedding Hierarchical Structures for Venue Category Representation

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue C ategory E mbedding M odel named Hier-CEM , which generates a latent representation for each venue category by embedding the Hier archical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.

Dual-View Conditional Variational Auto-Encoder for Emotional Dialogue Generation

Emotional dialogue generation aims to generate appropriate responses that are content relevant with the query and emotion consistent with the given emotion tag. Previous work mainly focuses on incorporating emotion information into the sequence to sequence or conditional variational auto-encoder (CVAE) models, and they usually utilize the given emotion tag as a conditional feature to influence the response generation process. However, emotion tag as a feature cannot well guarantee the emotion consistency between the response and the given emotion tag. In this article, we propose a novel Dual-View CVAE model to explicitly model the content relevance and emotion consistency jointly. These two views gather the emotional information and the content-relevant information from the latent distribution of responses, respectively. We jointly model the dual-view via VAE to get richer and complementary information. Extensive experiments on both English and Chinese emotion dialogue datasets demonstrate the effectiveness of our proposed Dual-View CVAE model, which significantly outperforms the strong baseline models in both aspects of content relevance and emotion consistency.

A comprehensive overview of miRNA targeting drought stress resistance in plants

MicroRNAs (miRNAs) are essential nonprotein-coding genes. In a range of organisms, miRNAs has been reported to play an essential role in regulating gene expressions at post-transcriptional level. They participate in most of the stress responsive processes in plants. Drought is an ultimate abiotic stress that affects the crop production. Therefore understanding drought stress responses are essential to improve the production of agricultural crops. Throughout evolution, plants have developed their own defense systems to cope with the adversities of environmental stresses. Among defensive mechanisms include the regulations of gene expression by miRNAs. Drought stress regulates the expression of some of the functionally conserved miRNAs in different plants. The given properties of miRNAs provide an insight to genetic alterations and enhancing drought resistance in cereal crops. The current review gives a summary to regulatory mechanisms in plants as well as miRNAs response to drought stresses in cereal crops. Some possible approaches and guidelines for the exploitation of drought stress miRNA responses to improve cereal crops are also described.

Methods of Comparison of the Economic Order Quantity and Just-In-Time Restocking Technologies. The Case Study

Estimation of the optimal size of order is one of the key tasks in determining the parameters of the urban freight restocking system. The existing analytical models and methods are considering each technology separately and they do not compare the Economic Order Quantity (EOQ) and Justin-tme (JIT) restocking technologies. The purpose of this research was to evaluate efficiency of the JIT and EOQ restocking technologies. The research would help in selecting the delivery model, analyzing functioning of existing JIT and EOQ models. The article presents an approach to determining the comparison in organizing supplies to the retailer. For this, the two supply models were compared. The Just-in-Time model is characterised by costs that are spend on transportation. The Economic Order Quantity model includes costs of transportation and storage in a warehouse. After calculations, application of the Just-in-Time model in the given conditions was determined.

Low Resource Neural Machine Translation: Assamese to/from Other Indo-Aryan (Indic) Languages

Machine translation (MT) systems have been built using numerous different techniques for bridging the language barriers. These techniques are broadly categorized into approaches like Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). End-to-end NMT systems significantly outperform SMT in translation quality on many language pairs, especially those with the adequate parallel corpus. We report comparative experiments on baseline MT systems for Assamese to other Indo-Aryan languages (in both translation directions) using the traditional Phrase-Based SMT as well as some more successful NMT architectures, namely basic sequence-to-sequence model with attention, Transformer, and finetuned Transformer. The results are evaluated using the most prominent and popular standard automatic metric BLEU (BiLingual Evaluation Understudy), as well as other well-known metrics for exploring the performance of different baseline MT systems, since this is the first such work involving Assamese. The evaluation scores are compared for SMT and NMT models for the effectiveness of bi-directional language pairs involving Assamese and other Indo-Aryan languages (Bangla, Gujarati, Hindi, Marathi, Odia, Sinhalese, and Urdu). The highest BLEU scores obtained are for Assamese to Sinhalese for SMT (35.63) and the Assamese to Bangla for NMT systems (seq2seq is 50.92, Transformer is 50.01, and finetuned Transformer is 50.19). We also try to relate the results with the language characteristics, distances, family trees, domains, data sizes, and sentence lengths. We find that the effect of the domain is the most important factor affecting the results for the given data domains and sizes. We compare our results with the only existing MT system for Assamese (Bing Translator) and also with pairs involving Hindi.

A Comprehensive Survey of Attacks without Physical Access Targeting Hardware Vulnerabilities in IoT/IIoT Devices, and Their Detection Mechanisms

With the advances in the field of the Internet of Things (IoT) and Industrial IoT (IIoT), these devices are increasingly used in daily life or industry. To reduce costs related to the time required to develop these devices, security features are usually not considered. This situation creates a major security concern. Many solutions have been proposed to protect IoT/IIoT against various attacks, most of which are based on attacks involving physical access. However, a new class of attacks has emerged targeting hardware vulnerabilities in the micro-architecture that do not require physical access. We present attacks based on micro-architectural hardware vulnerabilities and the side effects they produce in the system. In addition, we present security mechanisms that can be implemented to address some of these attacks. Most of the security mechanisms target a small set of attack vectors or a single specific attack vector. As many attack vectors exist, solutions must be found to protect against a wide variety of threats. This survey aims to inform designers about the side effects related to attacks and detection mechanisms that have been described in the literature. For this purpose, we present two tables listing and classifying the side effects and detection mechanisms based on the given criteria.

Anticipation, Simultaneity/Consecutivity and Distinctive Features in Phonology

The notion of distinctive features has had a firm position in phonology since the time of the Prague Linguistic Circle and especially that of one of its representatives, Roman Jakobson, whose well-known delimitation of a phoneme as “a bundle of distinctive features” (Jakobson, 1962, p. 421), that is, a set of simultaneous distinctive features, has inspired many scholars. Jakobson’s attempt “to analyse the distribution of distinctive features along two axes: that of simultaneity and that of successiveness” (ibid., p. 435) helped cover several phonetic and/or phonological processes and phenomena. Distinctive features, although theoretical constructs (Giegerich, 1992, p. 89), reflect phonetic, that is, articulatory and acoustic, properties of sounds. In the flow of speech, some features tend to influence the neighbouring phonemes. Sometimes speech organs produce something that the brain just ‘plans’ to produce (anticipatory speech errors). There are situations where it seems as if the successive organization of phonemes went hand in hand with the simultaneous nature of certain articulatory characteristics of those phonemes (the transgression of consonants and inherence of vowels in Romportl’s theory), or the given feature seems to be anticipated by the preceding segment. This is the case with nasalization and/or anticipatory coarticulation, as well as regressive (anticipatory) assimilation. In addition, simultaneity/consecutivity is a decisive criterion for the difference between the so-called complex segments, as specified in Feature Geometry, and simple segments (Duanmu, 2009). Moreover, the phonological opposition of simultaneity- successivity (that is, consecutivity) itself functions as a feature making a difference between segmental and suprasegmental elements in the sound system of a language, as was first mentioned by Harris (1944), later indicated by Jakobson (1962) and then fully developed by Sabol (2007, 2012).

On determining the parameters of face blocks during mining operations with backhoes

The backhoe hydraulic shovels (backhoes) have been widely used in open-pit mining operations in the Kuznetsk coal basin since the early 2000s. Now their number is growing every year, both because new operations begin on new sites, and because of the backhoe advantages. However, up to now parameters of their operation, more particularly the parameters of the face blocks, are often taken according to the actual data, i.e. based on the accumulated experience of the mining companies, and are not calculated using the approved scientifically validated methods. In the given article the authors give some preconditions for creation of a uniform methodology to calculate parameters of the face blocks depending on mining-geological and miningtechnical conditions. It is noted that the manufacturers of excavation equipment quote the data corresponding to the maximum possible parameters of the equipment operation, in particular the height of the excavated layer, as a result of which it is rather difficult to apply this in practice. Preliminary conclusions about the need to take into account the diversity of the above conditions and their interrelation with the parameters of a particular model of the excavation and loading equipment have been made.