Reviewerly/exHarmony huggingface dataset:
Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA

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Smart Street Litter Detection and Classification Based on Faster R-CNN and Edge Computing

Cleanliness of city streets has an important impact on city environment and public health. Conventional street cleaning methods involve street sweepers going to many spots and manually confirming if the street needs to be clean. However, this method takes a substantial amount of manual operations for detection and assessment of street’s cleanliness which leads to a high cost for cities. Using pervasive mobile devices and AI technology, it is now possible to develop smart edge-based service system for monitoring and detecting the cleanliness of streets at scale. This paper explores an important aspect of cities — how to automatically analyze street imagery to understand the level of street litter. A vehicle (i.e. trash truck) equipped with smart edge station and cameras is used to collect and process street images in real time. A deep learning model is developed to detect, classify and analyze the diverse types of street litters such as tree branches, leaves, bottles and so on. In addition, two case studies are reported to show its strong potential and effectiveness in smart city systems.
Multiple-source domain adaptation with generative adversarial nets

Current unsupervised domain adaptation (UDA) methods based on GAN (Generative Adversarial Network) architectures assume that source samples arise from a single distribution. These methods have shown compelling results by finding the transformation between source and target domains to reduce the distribution divergence. However, the one-to-one assumption renders the existing GAN-based UDA methods ineffective in a more realistic scenario that source samples are typically collected from diverse sources. In this paper, we present a novel GAN-enabled framework, which we call Multi-Source Adaptation Network (MSAN), for multiple-source domain adaptation (MDA) to mitigate the domain shifts between multiple source domains and the target domain. The proposed framework consists of multiple GAN architectures to learn bidirectional transformations between the source domains and the target domain efficiently and simultaneously. Technically, we introduce a joint feature space to guide the multi-level consistency constraints across all the transformations, in order to preserve the domain-invariant pattern and endow the discriminative power for the unlabeled target samples simultaneously during the adaptation. Moreover, the proposed model can naturally be used to enlarge the target dataset by utilizing the synthetic target images (with ground-truth labels from different source domains) and the pseudo-labeled target images, thereby allowing constructing the target-specific classifier in an unsupervised manner. Experiments demonstrate that our models exceed state-of-the-art results for MDA tasks on several benchmark datasets.
Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers

Efficient resource management in a Cloud data center relies on minimizing energy consumption and utilizing physical resource efficiently while maintaining the service-level agreement (SLA) at its highest level. To achieve this goal, dynamically consolidating virtual machines (VMs) is considered a promising method, because it eliminates the hotspots resulting from overloaded hosts and switches the underloaded hosts to sleep mode through the live migration of VMs. However, during the consolidation, each VM migration consumes additional resource, leading to performance degradation and SLA violation. To address this issue, this study proposes a novel adaptive performance-to-power-ratio (PPR)-aware dynamic VM consolidation framework based on both the predicted resource utilization and PPR of the heterogeneous hosts to resolve the trade-off of performance and energy. The proposed framework consists of four stages: (1) host overload detection based on residual available computing capacity; (2) selection of the appropriate VMs for migration from the overloaded hosts based on minimum data transfer; (3) host underload detection based on multi-criteria Z-score approach; (4) allocating the VMs selected for migration from the overloaded and underloaded hosts based on the modified power-aware best-fit decreasing algorithm. To validate the reliability and scalability of the proposed method, we performed experimental evaluation in both real and simulated environments. The experimental results demonstrate that the proposed approach can reduce the energy consumption effectively and ensure maximal conformity to the quality of service (QoS) requirements across heterogeneous infrastructures, in comparison with the existing competitive approaches.
PV-DAE: A hybrid model for deceptive opinion spam based on neural network architectures

Opinion review is of great importance for both customers and organizations. Indeed, it helps customers in buying decisions and represents a valuable feedback for the companies, allowing them to improve their productions. However, numerous greedy companies resort to fake reviews in order to influence the customer and brighten the brand image, or to defame the one of their competitors. Various models are proposed in order to detect deceptive opinion reviews. Most of these models adopt traditional methods focusing on feature extraction and traditional classifiers. Unfortunately, these models do not capture the semantic aspect while ignoring the opinion’s context. In order to tackle this issue, we propose a new approach based on Paragraph Vector Distributed Bag of Words (PV-DBOW) and the Denoising Autoencoder (DAE). The proposed customized model provides a strong representation which is based on a global representation of the opinions while preserving their semantics. Indeed, the embedding vectors capture the semantic meaning of all words in the context of each opinion. The generated review representations are fed into a fully connected neural network in order to detect deceptive opinion spam. The obtained results concerning the deception dataset show that our model is effective and outperforms the existing state-of-the-art methodologies.
Meta-learning Priors for Efficient Online Bayesian Regression

Gaussian Process (GP) regression has seen widespread use in robotics due to its generality, simplicity of use, and the utility of Bayesian predictions. The predominant implementation of GP regression is a nonparametric kernel-based approach, as it enables fitting of arbitrary nonlinear functions. However, this approach suffers from two main drawbacks: (1) it is computationally inefficient, as computation scales poorly with the number of samples; and (2) it can be data inefficient, as encoding prior knowledge that can aid the model through the choice of kernel and associated hyperparameters is often challenging and unintuitive. In this work, we propose ALPaCA, an algorithm for efficient Bayesian regression which addresses these issues. ALPaCA uses a dataset of sample functions to learn a domain-specific, finite-dimensional feature encoding, as well as a prior over the associated weights, such that Bayesian linear regression in this feature space yields accurate online predictions of the posterior predictive density. These features are neural networks, which are trained via a meta-learning (or “learning-to-learn”) approach. ALPaCA extracts all prior information directly from the dataset, rather than restricting prior information to the choice of kernel hyperparameters. Furthermore, by operating in the weight space, it substantially reduces sample complexity. We investigate the performance of ALPaCA on two simple regression problems, two simulated robotic systems, and on a lane-change driving task performed by humans. We find our approach outperforms kernel-based GP regression, as well as state of the art meta-learning approaches, thereby providing a promising plug-in tool for many regression tasks in robotics where scalability and data-efficiency are important.
Efficient two-party privacy-preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing

Nowadays, cloud computing has developed well and been applied in many kinds of areas. However, privacy is still the most challenging problem which obstructs it being applied in some privacy-sensitive fields, such as finance and government. Advanced cryptographic algorithms provide data privacy with encryption, which can also support computation on such encrypted data. However, new challenge arises when such ciphertexts come from different parties. In particular, how to execute collaboratively data mining on encrypted data coming from different parties is a key issue from cloud service point of view. This paper focuses on privacy problem on outsourced k-means clustering scheme for two parties. In particular, each party’s data are encrypted only once and then stored in cloud. The proposed privacy-preserving k-means collaborative clustering protocol is executed mainly at the cloud, with O(k(m+n)) rounds of interactions among the two parties and the cloud, where m and n represent the total numbers of records for the two parties, respectively. It is shown that the protocol is secure in the semi-honest security model and in the malicious model in which only one party is corrupted during the process of centroids re-computation. Both theoretical and experimental analysis of the proposed scheme are also provided.
Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge

The task of electronic medical record named entity recognition (NER) refers to automatically identify all kinds of named entities in the medical record text. Chinese clinical NER remains a major challenge. One of the main reasons is that Chinese word segmentation will lead to the wrong downstream works. Besides, existing methods only use the information of the general field, not consider the knowledge from field of medicine. To address these issues, we propose a dynamic embedding method based on dynamic attention which combines features of both character and word in embedding layer. Domain knowledge is provided by word vector trained by domain dataset. In addition, spatial attention is added to enable the model to obtain more and more effective context encoding information. Finally, we conduct extensive experiments to demonstrate the effectiveness of our proposed algorithm. Experiments on CCKS2017 and Common dataset shows that the proposed method outperforms the baseline.
An optimal support vector machine based classification model for sentimental analysis of online product reviews

In present days, recent developments in data analytics take place that allows the identification of underlining trends through effective computational models. In several e-commerce and social platform, massive number of online product reviews is posted by end users that significantly help the developers with priceless insight while designing the products. This paper presents a new cluster based classification model for online product reviews. The presented model comprises several processes. Initially, support vector machine (SVM) based classification model is applied to classify the product reviews. Then, confusion matrix is generated to consider the possibilities of every consumer purchasing the product. Next, K-means clustering technique is applied to cluster the available data into two groups. At the next stage, sentimental analysis approach is employed to extracting the features. Finally, fuzzy based soft set theory is applied to determine the possibility of the customer to purchase the product effectively. The experimental validation of the presented model takes place on ipod dataset. The simulation outcome pointed out the superior characteristics of the presented model under several aspects.
Measuring text similarity based on structure and word embedding

The problem of finding the similarity between natural language sentences is crucial for many applications in Natural Language Processing (NLP). An accurate calculation of similarity between sentences is highly needed. Many approaches depend on word-to-word similarity to measure sentence similarity. This paper proposes a new approach to improve the accuracy of the sentence similarity calculation. The proposed approach combines different similarity measures in the calculation of sentence similarity. In addition to traditional word-to-word similarity measure, the proposed approach exploits sentence semantic structure. Discourse representation structure (DRS) which is a semantic representation for natural sentences is generated and used to calculated structure similarity. Furthermore, word order similarity is measured to consider the order of words in sentences. Experiments show that exploiting structural information achieves good results. Moreover, the proposed method outperforms the current approaches on a standard benchmark dataset achieving 0.8813 Pearson correlation with human similarity.
Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies - PV Agigea and PV Giurgiu located in Romania

Nowadays, plenty of data is continuously pouring from the PhotoVoltaic Power Plants (PV) monitoring systems and sensors that could be successfully handled by big data technologies. This paper proposes a methodology that automatically collects the data logs from sensors installed on PV arrays, inverters and weather stations, checks the health status of the PV components, forecasts the generated power for each inverter based on its real operating conditions and the predicted irradiance and finally provides useful insights of the PV system based on the Key Performance Indicators (KPI) using big data technologies. The Ultra-Short-Term Forecast (USTF) algorithm provides the estimations of irradiance and generated power for the next 30 min and is applied on a sliding time window interval. The algorithm uses a Feed-Forward Artificial Neural Network (FF-ANN) and, to significantly reduce the number of iterations, we propose a backtracking adjustment of the learning rate that enables faster convergence reducing the computational time that is essential for USTF. Two data sets from PV Agigea 0.5 MW and PV Giurgiu 7.5 MW, located in the South-East and South of Romania, that consist in data logs from inverters and arrays, are used for simulation. The exhaustive analyses are performed for PV Agigea (including KPI calculation), while PV Giurgiu data set was mainly used to check the scalability and replicability of the algorithm.
Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

In some special rescue scenarios, the needed goods should be transported by drones because of the landform. Therefore, in this study, we investigate a multi-objective vehicle routing problem with time window and drone transportation constraints. The vehicles are used to transport the goods and drones to customer locations, while the drones are used to transport goods vertically and timely to the customer. Three types of objectives are considered simultaneously, including minimization of the total energy consumption of the trucks, total energy consumption of the drones, and the total number of trucks. An improved artificial bee colony algorithm is designed to solve the problem. In the proposed algorithm, each solution is represented by a two-dimensional vector, and the initialization method based on the Push-Forward Insertion Heuristic is embedded. To enhance the exploitation abilities, an improved employed heuristic is developed to perform detailed local search. Meanwhile, a novel scout bee strategy is presented to improve the global search abilities of the proposed algorithm. Several instances extended from the Solomon instances are used to test the performance of the proposed improved artificial bee colony algorithm. Experimental comparisons with the other efficient algorithms in the literature verify the competitive performance of the proposed algorithm.
Data frontiers and frontiers of power in (higher) education: a view of/from the Global South

‘Data as technology’ has always been, and continues to be an essential part of the structuring of South African society and education, during and post-colonialism and post-apartheid. In the reconfiguration of South African education post-apartheid, student data constitutes a data frontier as un-mapped, under-utilised and ready for the picking. This article maps the data frontier in the nexus of higher education in the global/colonial present and the data imaginary that provides a particular vision in service of a neoliberal discursive position and ideological orientation. As such, the data frontier acts as ‘generative matrix’ for educational policy attempting to address the legacies of colonialism and apartheid. The value contribution of this article lies in its positioning of the data imaginary in the context of a neoliberal approach to education in the context of the Global South.
A new fuzzy strategy for size and topology optimization of truss structures

Most of the metaheuristic optimization techniques are the general optimization tools with the ability of solving various class of problems. However, in the case of more complex engineering problems, adding extra customizing procedure(s) can improve their performance. In this regard, the current study deals with introducing a new auxiliary fuzzy decision mechanism to enhance these methods’ capability on handling the structural size and topology optimization problems. The proposed mechanism aims to reduce the complexity level of the structural size and topology optimization problems by converting their complex search spaces into simpler fuzzy domains. Introduced fuzzy mechanism, during the optimization process, permanently monitors the population updating process and emphasizes either size or topology search behavior of each agent. Proposed mechanism evaluates agents via two predefined concepts so-called Normalized Objective Function (NOFi) and Normalized Members Density (NMDi). Since the presented fuzzy strategy designed as an independent regulator module, it can be integrated with different optimization algorithms. In the current work, it is combined with the Interactive Search Algorithm (ISA) optimization method and the compound method is named as Fuzzy Tuned Interactive Search Algorithm (FTISA). Eventually, its performance is comparatively assessed on solving a number of structural size and topology optimization problems with dynamic and static constraints. Achieved results demonstrate that the introduced fuzzy strategy not only significantly enhances the computational cost of the process but also improves the accuracy of the solutions and stability of the algorithm.
High throughput and low area architectures of secure IoT algorithm for medical image encryption

The favor that lightweight encryption algorithms have gained over the recent years regarding data and image security in a resource constrained environment of the smart device sort in Internet of Things (IoT) is undeniable. Secure IoT (SIT) is a popular encryption algorithm that tends to consume less hardware resources due to simple operation, which serves a welcome advantage, since it is precisely the reduction of area and power consumption. In this paper, high-speed and low-area architectures are proposed for SIT algorithm under resource constrained applications. The proposed pipelined architecture is useful in high frequency applications, and proposed serial architecture is useful for low area requirement, leads to reduce the cost incurred on hardware. The proposed design can support four different block sizes of an input medical ultrasonic image, namely 32 × 32, 64 × 64, 128 × 128 and 256 × 256 along with Finite State Machine (FSM) controller. The dynamic size of block selection technique saves significant amount of clock cycles during image encryption. The proposed designs have implemented in FPGA XC5VLX50T-3ff1136 device and achieved maximum operating frequency of 287.51 MHz in pipelined architecture and number of slices 46 in serial architecture. PRESENT is an efficient lightweight block cipher having 73 slices in serial architecture whereas proposed serial architecture provides 58.69% improvement in area. LEA is a popular Feistel block cipher generates maximum frequency of 217 MHz in pipelined architecture, but at the same time proposed pipelined architecture provides 32.25% enhancement in speed.
Accurate and robust neural networks for face morphing attack detection

Artificial neural networks tend to use only what they need for a task. For example, to recognize a rooster, a network might only considers the rooster’s red comb and wattle and ignores the rest of the animal. This makes them vulnerable to attacks on their decision making process and can worsen their generality. Thus, this phenomenon has to be considered during the training of networks, especially in safety and security related applications. In this paper, we propose neural network training schemes, which are based on different alternations of the training data, to increase robustness and generality. Precisely, we limit the amount and position of information available to the neural network for the decision making process and study their effects on the accuracy, generality, and robustness against semantic and black box attacks for the particular example of face morphing attacks. In addition, we exploit layer-wise relevance propagation (LRP) to analyze the differences in the decision making process of the differently trained neural networks. A face morphing attack is an attack on a biometric facial recognition system, where the system is fooled to match two different individuals with the same synthetic face image. Such a synthetic image can be created by aligning and blending images of the two individuals that should be matched with this image. We train neural networks for face morphing attack detection using our proposed training schemes and show that they lead to an improvement of robustness against attacks on neural networks. Using LRP, we show that the improved training forces the networks to develop and use reliable models for all regions of the analyzed image. This redundancy in representation is of crucial importance to security related applications.