Reviewerly/exHarmony huggingface dataset:
Integrating the soybean-maize-chicken value chains to attain nutritious diets in Tanzania

Abstract In Tanzania, diets are dominated by starchy staple crops such as maize, levels of malnutrition are high and largely attributed to lack of dietary diversity. We employed fuzzy cognitive mapping to understand the current soybean, maize and chicken value chains, to highlight stakeholder relationships and to identify entry points for value chain integration to support nutritious diets in Tanzania. The fuzzy cognitive maps were constructed based on information gathered during household interviews with 569 farming households, followed by a participatory workshop with 54 stakeholders involved in the three value chains. We found that the soybean, maize and chicken value chains were interconnected, particularly at the level of the smallholder farming systems and at processing facilities. Smallholder farming households were part of one or more value chains. Chicken feed is an important entry point for integrating the three value chains, as maize and soybean meal are the main sources of energy and protein for chicken. Unlike maize, the utilization of soybean in chicken feed is limited, mainly due to inadequate quality of processing of soybean grain into meal. As a result, the soybean grain produced by smallholders is mainly exported to neighbouring countries for further processing, and soybean meal is imported at relatively high prices. Enhancing local sourcing and adequate processing of soybean, coupled with strengthening the integration of smallholder farmers with other soybean, maize and chicken value chain actors offers an important opportunity to improve access to nutritious diets for local people. Our method revealed the importance of interlinkages that integrate the value chains into a network within domestic markets.
Real-Time Surveillance System based on Facial Recognition using YOLOv5

Facial recognition using deep learning techniques is now a rapidly growing and widely applied aspect of real-time surveillance systems with broad range of applications in every field. Recognizing multiple faces in real-time is very challenging due to adverse environmental conditions and occlusion effects. YOLOv5 is the current state-of-the-art algorithm for real-time facial recognition with very limited experimental analysis. In this paper YOLOv5 has been trained from scratch and tested on FDDB and customized dataset from real-time video feed. Experiments show 87% accuracy on FDDB while 94% accuracy on the customized dataset. The paper also presents comparative analysis of the results with the previous versions of YOLOv5 (YOLOv3 and YOLOv4). The algorithm is also tested on real-time environment and has the capability to detect multiple faces with maximum accuracy.
Columnar storage and list-based processing for graph database management systems

We revisit column-oriented storage and query processing techniques in the context of contemporary graph database management systems (GDBMSs). Similar to column-oriented RDBMSs, GDBMSs support read-heavy analytical workloads that however have fundamentally different data access patterns than traditional analytical workloads. We first derive a set of desiderata for optimizing storage and query processors of GDBMS based on their access patterns. We then present the design of columnar storage, compression, and query processing techniques based on these desiderata. In addition to showing direct integration of existing techniques from columnar RDBMSs, we also propose novel ones that are optimized for GDBMSs. These include a novel list-based query processor, which avoids expensive data copies of traditional block-based processors under many-to-many joins, a new data structure we call single-indexed edge property pages and an accompanying edge ID scheme, and a new application of Jacobson's bit vector index for compressing NULL values and empty lists. We integrated our techniques into the GraphflowDB in-memory GDBMS. Through extensive experiments, we demonstrate the scalability and query performance benefits of our techniques.
Automatic content‐based georeferencing of historical topographic maps

Abstract Old maps are more than a cultural artefact: they are data. Data about the past still hold value for science and decision‐making today. Libraries and archives have come a long way in digitising their inventories of thousands, sometimes millions, of historical maps using high‐resolution scanning. Unfortunately, even with digital images the rich spatial and semantic information is inaccessible for people without a strong background in history and cartography. Only with georeferencing can historical maps be used in GIS and thus processed and compared with modern geospatial data. We introduce content‐based image retrieval to automatically localise and georeference map images from topographic map series. We align the maps by extracting a subset of their symbols and cross‐referencing them with online reference data from OpenStreetMap. We demonstrate our method with the Karte des Deutschen Reiches at 1: 100,000 scale, obtaining 96% correct location predictions and a median georeferencing error of 101 m.
Normalization Influence on ANN-Based Models Performance: A New Proposal for Features’ Contribution Analysis

Artificial Neural Networks (ANNs) are weighted directed graphs of interconnected neurons widely employed to model complex problems. However, the selection of the optimal ANN architecture and its parameters training is not enough to obtain reliable models. The data preprocessing stage is fundamental to improve the model’s performance. Specifically, Feature Normalisation (FN) is commonly utilised to remove the features’ magnitude aiming at equalising the features’ contribution to the model training. Nevertheless, this work demonstrates that the FN method selection affects the model performance. Also, it is well-known that ANNs are commonly considered a "black box" due to their lack of interpretability. In this sense, several works aim to analyse the features’ contribution to the network for estimating the output. However, these methods, specifically those based on network’s weights, like Garson’s or Yoon’s methods, do not consider preprocessing factors, such as dispersion factors, previously employed to transform the input data. This work proposes a new features’ relevance analysis method that includes the dispersion factors into the weight matrix analysis methods to infer more precisely each feature’s actual contribution to the network output. Besides, in this work, the Proportional Dispersion Weights (PWD) are proposed as explanatory factors of similarity between models’ performance results. The conclusions from this work improve the understanding of the features’ contribution to the model that enhances the feature selection strategy, which is fundamental for reliably modelling a given problem.
Fragile Neural Network Watermarking with Trigger Image Set

Recent studies show that deep neural networks are vulnerable to data poisoning and backdoor attacks, both of which involve malicious fine tuning of deep models. In this paper, we first propose a black-box based fragile neural network watermarking method for the detection of malicious fine tuning. The watermarking process can be divided into three steps. Firstly, a set of trigger images is constructed based on a user-specific secret key. Then, a well trained DNN model is fine-tuned to classify the normal images in training set and trigger images in trigger set simultaneously in a two-stage alternate training manner. Fragile watermark is embedded by this means while keeping model's original classification ability. The watermarked model is sensitive to malicious fine tuning and will produce unstable classification results of the trigger images. At last, the integrity of the network model can be verified by analyzing the output of watermarked model with the trigger image set as input. The experiments on three benchmark datasets demonstrate that our proposed watermarking method is effective in detecting malicious fine tuning.
Prototype generation in the string space via approximate median for data reduction in nearest neighbor classification

Abstract The k -nearest neighbor ( k NN) rule is one of the best-known distance-based classifiers, and is usually associated with high performance and versatility as it requires only the definition of a dissimilarity measure. Nevertheless, k NN is also coupled with low-efficiency levels since, for each new query, the algorithm must carry out an exhaustive search of the training data, and this drawback is much more relevant when considering complex structural representations, such as graphs, trees or strings, owing to the cost of the dissimilarity metrics. This issue has generally been tackled through the use of data reduction (DR) techniques, which reduce the size of the reference set, but the complexity of structural data has historically limited their application in the aforementioned scenarios. A DR algorithm denominated as reduction through homogeneous clusters (RHC) has recently been adapted to string representations but as obtaining the exact median value of a set of string data is known to be computationally difficult, its authors resorted to computing the set-median value. Under the premise that a more exact median value may be beneficial in this context, we, therefore, present a new adaptation of the RHC algorithm for string data, in which an approximate median computation is carried out. The results obtained show significant improvements when compared to those of the set-median version of the algorithm, in terms of both classification performance and reduction rates.
Differentially Quantized Gradient Descent

Consider the following distributed optimization scenario.A worker has access to training data that it uses to compute the gradients while a server decides when to stop iterative computation based on its target accuracy or delay constraints.The server receives all its information about the problem instance from the worker via a rate-limited noiseless communication channel.We introduce the principle we call differential quantization (DQ) that prescribes compensating the past quantization errors to direct the descent trajectory of a quantized algorithm towards that of its unquantized counterpart.Assuming that the objective function is smooth and strongly convex, we prove that differentially quantized gradient descent (DQ-GD) attains a linear contraction factor of max{σGD, ρn2 -R }, where σGD is the contraction factor of unquantized gradient descent (GD), ρn ≥ 1 is the covering efficiency of the quantizer, and R is the bitrate per problem dimension n.Thus at any R ≥ log 2 ρn/σGD bits, the contraction factor of DQ-GD is the same as that of unquantized GD, i.e., there is no loss due to quantization.We show a converse demonstrating that no algorithm within a certain class can converge faster than max{σGD, 2 -R }.Since quantizers exist with ρn → 1 as n → ∞ (Rogers, 1963), this means that DQ-GD is asymptotically optimal.In contrast, naively quantized GD where the worker directly quantizes the gradient barely attains σGD + ρn2 -R .The principle of differential quantization continues to apply to gradient methods with momentum such as Nesterov's accelerated gradient descent, and Polyak's heavy ball method.For these algorithms as well, if the rate is above a certain threshold, there is no loss in contraction factor obtained by the differentially quantized algorithm compared to its unquantized counterpart, and furthermore, the differentially quantized heavy ball method attains the optimal contraction achievable among all (even unquantized) gradient methods.Experimental results on least-squares problems validate our theoretical analysis.
Simultaneous feature and instance selection in big noisy data using memetic variable neighborhood search

In smart factories, the data collected by Internet-of-things sensors is enormous and includes a lot of noise and missing values. To address this big data problem, metaheuristic is one of the main approaches to data preprocessing, i.e., instance selection or feature selection before training the model. Most previous works on metaheuristic approaches rarely considered simultaneous instance selection and feature selection, and rarely focused on addressing big noisy data. Consequently, this work proposes a hybrid memetic algorithm (MA) with variable neighborhood search (VNS) to simultaneously select instances and features, in which MA performs excellently in data selection; and VNS has been shown to perform well in local search. To evaluate the performance of the proposed algorithm, this work creates simulation data by combining the datasets from the UCI with noisy data. The proposed algorithm for simultaneous feature and instance selection is adopted to reduce the simulation data, and then the reduced data is adopted to train a predictive model for later performance evaluation of model testing. As compared with other metaheuristics, the proposed algorithm achieves a balance between exploration and exploitation. Additionally, the results show that the proposed algorithm is more robust than other feature selection methods.
Design and validation of a demand-oriented single-camera stereo-DIC system with a four-mirror adapter

Four-mirror-assisted single-camera stereo-digital image correlation (stereo-DIC) system overperforms conventional two-camera stereo-DIC system in terms of system compactness, cost investment and easy implementation. However, how to design a suitable four-mirror adaptor orienting a specific demand has not been clearly described in previous researches. To address this issue and satisfy practical requirements encountered in real applications, we proposed a design strategy for determining the optimal structure of a four-mirror adaptor. Specifically, to optimize the structural parameters of a four-mirror adaptor, the proposed design strategy comprehensively considers various factors, including the desired field of view (FOV), working distance, measurement accuracy, system compactness as well as the parameters of available devices (camera and lens). After implementing the algorithm based on the degrees of freedom (DOF) of the imaging system, an optimal solution can be determined from all the solutions that can satisfy the identical requirements. The designed four-mirror adapter is then printed by a 3D printer, and can be directly attached to the lens for single-camera pseudo-stereo imaging. Further, two specific design paradigms demonstrated the correctness and effectiveness of the proposed design strategy. It is expected that the proposed method can help the researchers to construct a demand-satisfied yet compact four-mirror-assisted single-camera stereo-DIC system.
Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image

Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships.
WRS: A Novel Word-embedding Method for Real-time Sentiment with Integrated LSTM-CNN Model

Artificial Intelligence (AI) is a research-focused technology in which Natural Language Processing (NLP) is a core technology in AI. Sentiment Analysis (SA) aims to extract and classify the people's opinions by NLP. The Machine Learning (ML) and lexicon dictionaries have limited competency to efficiently analyze massive live media data. Recently, deep learning methods significantly enrich the accuracy of recent sentiment models. However, the existing methods provide the aspect-based extraction that reduces individual word accuracy if a sentence does not follow the aspect information in real-time. Therefore, this paper proposes a novel word embedding method for the real-time sentiment (WRS) for word representation. The WRS's novelty is a novel word embedding method, namely, Word-to-Word Graph (W2WG) embedding that utilizes the Word2Vec approach. The WRS method assembles the different lexicon resources to employ the W2WG embedding method to achieve the word feature vector. Robust neural networks leverage these features by integrating LSTM and CNN to improve sentiment classification performance. LSTM is utilized to store the word sequence information for the effective real-time SA, and CNN is applied to extract the leading text features for sentiment classification. The experiments are conducted on Twitter and IMDB datasets. The results demonstrate our proposed method's effectiveness for real-time sentiment classification.
Three Degree Binary Graph and Shortest Edge Clustering for re-ranking in multi-feature image retrieval

Graph methods have been widely employed in re-ranking for image retrieval. Although we can effectively find visually similar images through these methods, the ranking lists given by those approaches may contain some candidates which appear to be irrelevant to a query. Most of these candidates fall into two categories: (1) the irrelevant outliers located near to the query images in a graph; and (2) the images from another cluster which close to the query. Therefore, eliminating these two types of images from the ordered retrieval sets is expected to further boost the retrieval precision. In this paper, we build a Three Degree Binary Graph (TDBG) to eliminate the outliers and utilize a set-based greedy algorithm to reduce the influence of adjacent manifolds. Moreover, a multi-feature fusion method is proposed to enhance the retrieval performance further. Experimental results obtained on three public datasets demonstrate the superiority of the proposed approach.
SCIPS: A serious game using a guidance mechanic to scaffold effective training for cyber security

Training effective simulation scenarios presents numerous challenges from a pedagogical point of view. Through application of the Conceptual Framework for e-Learning and Training (COFELET) as a pattern for designing serious games, we propose the use of the Simulated Critical Infrastructure Protection Scenarios (SCIPS) platform as a prospective tool for supporting the process of providing effective cyber security training. The SCIPS platform is designed to run different scenarios, such as examples in financial forecasting and business infrastructures, with an initial scenario developed in collaboration with industrial partners focusing on an electricity generation plant. Focus groups from these sources were conducted to identify design and developmental considerations for the platform. As an extension from the COFELET framework, we propose an intelligence scaffolding practice as a guidance mechanic taking the form of an agent within the scenario. The agent represents a major innovation in the system and we envisage a deep learning-based augmentation to further adapt towards the behavioural aspects of learners.
Cryptocurrencies Impact on Financial Markets: Some Insights on Its Regulation and Economic and Accounting Implications

Cryptocurrencies have become a trend (and, sometimes, a nightmare) for the financial sector. There are a multitude of news, opinions and criticisms about cryptocurrencies that need to be contextualized and pondered. This paper aims to explain some of the key legal issues regarding cryptocurrencies in the financial market and to analyze the highlights of one of the most recent regulatory proposals. The article introduces the real legal and financial reality surrounding cryptocurrencies and without which they could not develop. In line with the development of technology, this paper clarifies the volatility and the negativity of volatility for the development of cryptocurrencies, which are favoured by the development of accounting systems. Through a concrete example, the need for consistent accounting legislation and cash principles and rules observed in the importance of the cryptocurrency wallet and its implications on the current and traditional cash and bank-based management is incorporated in the present study.
Double Encryption Using Trigonometric Chaotic Map and XOR of an Image

In the most recent decades, a major number of image encryption plans have been proposed. The vast majority of these plans reached a high-security level; however, their moderate speeds because of their complicated processes ma... | Find, read and cite all the research you need on Tech Science Press
Adaptive Subspace Optimization Ensemble Method for High-Dimensional Imbalanced Data Classification

It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which the performance of classifiers is seriously affected and becomes poor. Although many approaches, such as resampling, cost-sensitive, and ensemble learning methods, have been proposed to deal with the skewed data, they are constrained by high-dimensional data with noise and redundancy. In this study, we propose an adaptive subspace optimization ensemble method (ASOEM) for high-dimensional imbalanced data classification to overcome the above limitations. To construct accurate and diverse base classifiers, a novel adaptive subspace optimization (ASO) method based on adaptive subspace generation (ASG) process and rotated subspace optimization (RSO) process is designed to generate multiple robust and discriminative subspaces. Then a resampling scheme is applied on the optimized subspace to build a class-balanced data for each base classifier. To verify the effectiveness, our ASOEM is implemented based on different resampling strategies on 24 real-world high-dimensional imbalanced datasets. Experimental results demonstrate that our proposed methods outperform other mainstream imbalance learning approaches and classifier ensemble methods.
A semisupervised learning model based on fuzzy min–max neural networks for data classification

Semisupervised learning (SSL) models are useful for undertaking classification problems with a small set of labeled samples and a large number of unlabeled samples. In this regard, the family of fuzzy min–max (FMM) neural networks offers the capability of online learning for addressing both unsupervised and supervised problems. As such, this paper proposes a novel two-stage SSL model based on FMM networks, denoted SSL–FMM. The first stage employs the unlabeled samples to generate a number of hyperboxes using the unsupervised FMM algorithm, while the second stage uses the labeled samples to associate the generated hyperboxes with their target classes using the supervised FMM algorithm. In addition, a neighborhood-labeling mechanism based on the Euclidean distance and hyperbox centroids is formulated to associate the unlabeled hyperboxes with the most likely target classes. A number of benchmark problems and a real-world case study are employed to evaluate the effectiveness of the proposed SSL–FMM model. The outcome indicates that SSL–FMM is able to use unlabeled samples effectively and improve the FMM performance, producing promising results compared with other SSL methods in the literature.