Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Evolutionary Multiobjective Optimization for Raw Ore Allocation in Mineral Processing

Raw ore allocation in mineral processing is crucial for improving the utilization ratio of nonrenewable raw mineral resources. Allocation of raw ore is a nonlinear, multiobjective programming problem. Such a problem is usually modeled under the assumption that the production process is stationary and the model parameters are deterministic. However, in practice, variations in equipment capability and runtime lead to frequent changes of the model parameters including the constraints. To address the above-mentioned issues, this paper formulates a raw ore allocation in mineral processing as a dynamic multiobjective optimization problem. To solve this problem, a new variant of the elitist nondominated sorting genetic algorithm (D-NSGA-II) is proposed, which employs NSGA-II as the basic component assisted by random immigrant scheme, a gradient-based local search strategy, and a mechanism for detecting environmental changes. Simulations are carried out and the results show that the proposed algorithm can efficiently achieve the Pareto front of the multiobjective raw ore allocation dynamic optimization problem. It can simultaneously converge fast while maintaining good population diversity, in particular, in the presence of large environmental changes.
A New Design of Reconfigurable SIW Antenna Conformal to Cylindrical Surface

This paper presents a new design of a conformal SIW antenna for beam scanning and 5G applications. The presented antenna is designed at 26 GHz. It is based on 10 L-shaped slots engraved on the broad wall of the substrate-integrated waveguide. The beam steering is controlled by using PIN-Diodes inserted in each slot to ensure switching and selecting the radiant elements for desired direction.
Estimating depressive status from voice

We developed an algorithm to estimate the depression status from a person's voice signal. In the experiment, we collected voice samples from patients with major depression. In addition, questionnaires concerning the patients' depressed mood were obtained. The voice signals were collected for the subjects' vocalizations of three types of long vowels. Next, acoustic features were calculated based on the speech. Subsequently, an algorithm was developed to estimate the severity of depression, judged by the HAM-D score, from the recorded voice samples. The results indicated that the algorithm performed well at estimating the severity of the HAM-D score using the acoustic features of the long vowels. Consequently, the algorithm also performed well at estimating the depressed mood, thus suggesting the utility of the algorithm for estimating depression conditions based on speech.
ANNOTATE: orgANizing uNstructured cOntenTs viA Topic labEls

With the advent of Big Data paradigm, filtering, retrieval, and linking of unstructured multi-modal data has become a necessity. Assigning topic labels to contents, that accurately capture the meaning and contextual information, is a fundamental problem in organizing unstructured data. The usage of manually-assigned tags for this purpose introduces inconsistencies because of different "surface forms". On the other hand, existing automated approaches either use hierarchical multi-label classification, or are unsupervised and rely on (undirected) graph measures leveraging taxonomies. While the former requires large training data set to learn the characteristics of each topic class, the latter lacks the flexibility to learn broad range of related topics and are less accurate.We propose a novel framework, ANNOTATE based on a small set of features and directed traversal of taxonomies to learn a broad spectrum of related topics using limited training data. We also show that our approach provides accurate labels for several domains without the need for re-training. For instance, the framework, trained on a small set of BBC news articles, exhibits close matches to user-generated tags for Quora documents. Experimental results, on the same model, for news classification and identifying aspects of Amazon product reviews, based on Amazon Mechanical Turk evaluation show our approach to be significantly better than state-of-the-art.We further present real-life case studies of our proposed framework for automatically tagging Quora posts, and topically segmenting, indexing and linking related YouTube videos (using our publicly available Chrome browser extension).
A Reinforcement Learning Method for Intermediate Point Enthalpy Control in Super-critical Power Unit

The intermediate point enthalpy is a significant indicator for the steam temperature in thermal power unit, which has a great impact on the safety and economy in power unit. The intermediate point enthalpy control becomes more difficult in supercritical power unit than in subcritical unit because of controlled plant's characteristics of non-linearity, large inertia and coupling between different input. In this paper, a reinforcement learning method using proximal policy optimization algorithm is proposed to operate the feed water following control scheme for coordinated control system in supercritical unit. Experiment indicates that the proposed method can achieve satisfactory control performance compared with feed-forward decoupling control.
Performance Analysis of DST-Based Intensity Modulated/Direct Detection (IM/DD) Systems for VLC

The dual functionality of assuring ‘illumination’ and “communication” contemporaneously by relying on sustainable and energy-efficient opto-electronic devices like light emitting diodes (LEDs) has enabled visible light communication to garner the attraction of research era. This paper proposes a discrete sine transform (DST)-based optical orthogonal frequency division multiplexing (OFDM) system bestowing as the simplest and alternative multicarrier scheme complying the requirements of intensity modulated and direct detection systems. Moreover, this paper addresses the most vital issue like peak to average power ratio (PAPR) that emerges principally due to the limited dynamic range of LEDs when exploiting DC-biased optical OFDM (DCO-OFDM). Therefore, this paper imparts DST spreading to DST-based DCO-OFDM (DST-S-DCO-OFDM), and consequently, the PAPR performance is compared with other PAPR reduction schemes for the same system. Furthermore, in order to serve multiple subscribers with high-data rates as well as to significantly reduce the amount of PAPR of the multicarrier system, this work investigates a DST-based single carrier frequency division multiple access (DST-SC-FDMA) and derives the mathematical analysis of the time-domain signal formats for different subcarrier mapping strategies like DST-based optical interleaved frequency division multiple access (DST-OIFDMA) and optical localized frequency division multiple access (DST-OLFDMA). The simulated results evidences that the reduction in PAPR for DST-S-DCO-OFDM is more predominant when compared with other PAPR reduction schemes and among the multiple access schemes, the DST-OIFDMA is more power efficient upon comparison with DST-OLFDMA. However, both the schemes exhibit a significant reduction in PAPR when compared with DST-based optical OFDM (DST-OOFDMA).
Adaptive MIMO Detector Using Reduced Search Space and Its Error Rate Estimator in Ultra Dense Network

In this paper, we analyzed the error rate of the sphere decoders with reduced search space (SD-RSS) and proposed a formula for estimating their error rate. With the instantaneous error rate information, an adaptive SD-RSSwas designed that could adjust the search space on-the-fly to minimize the complexity while meeting the error rate requirement specified by the standard. We focused on the ultradense network with severe inter-cell interference, which increases the detection complexity and complicates the error rate analysis. The accuracy of the error rate performance estimation was demonstrated by several simulations, and one numerical example was presented to show that the proposed adaptive SD-RSS can significantly reduce its average computational complexity while meeting the LTE block error rate requirement.
Stacked Denoising Auto-encoder Based Image Representation for Visual Loop Closure Detection

Loop closure detection is important in S-LAM (Simultaneous Location and Mapping) for its capability of relocation. Many techniques have been proposed such as Kalman filtering based methods. On the other hand, loop closure in the visual based SLAM can also be treated as an image retrieval problem. In recently years, deep learning is paid great attention and it is very appropriate for image classification and retrieval. However, deep learning usually askes for big data which may not be satisfied in visual based SLAM. In this paper, we proposed an unsupervised image retrieval method for loop closure detection. The SDA (Stacked Auto-encoder) is employed to translate images to high-dimensional representations, and then loop clousre detection is manipulated. The experiments show that, our method outperform the traditional BoW(Bag-of-Word) method in the ‘New College’ dataset and ‘City Centre’ dataset.
Analysis of Gene Expression Data of RPL10 Mutant T-Cell Leukemia by SEMsubPA

This paper describes the analysis of T-cell acute lymphoblastic leukemia (T-ALL) samples with an R98S missense mutation in ribosomal protein L10 (RPL10) compared to samples affected by T-ALL but without the mutation. The goal was to characterize the effect of RPL10 mutations on mRNA gene expression level. To this end, a novel tool called SEMsubPA was used, which allowed to detect significant KEGG sub-pathways and differentially expressed genes (DEGs) in one step. The tool exploits the potential of multi-group structural equation modeling for the discovery of the significant sub-pathways. Furthermore, it allows to test the significance of the connections between the genes in each significant sub-pathway. The most relevant components of the final biological network were characterized by Gene Ontology enrichment analysis based on Biological Process (BP) and Molecular Functions (MF). The analysis revealed key sub-pathways involved in necroptosis, MAPK signaling pathway and T-cell receptor signaling pathways. In addition, the network and enrichment analyses discovered key cancer genes such as AKT1, RIPK1, RIPK3, MYC and H1F1A as well as important molecular functions such as cellular oxidative stress, protein folding and kinase activity. Finally, the performance of SEMsubPA was compared against 3 other pathway and one sub-pathway analysis method. SEMsubPA was by far the best, detecting 81% of the total number of reference pathways, whereas the maximum performance of the other methods was 5%.
Discovery of User Preferences from Big Geospatial Data Using Topic Models

It is becoming common to apply location information for actual problems such as automatic routing, creating tour, and the prediction of user activity. In many of these cases, they use a geodata that is based on a user’s self check-in. In this study, to focus on a user’s daily behavior deeply, we use a large volume of geospatial data, which is detected regularly and automatically from a cell phone’s GPS system and the volume of data is totally different from the self check-in based. We can comprehend users activity in more detail than self check-in based data. The main purpose of this research is to extract and interpret user preferences from big geospatial data. Because the auto-detected data is big and spatial, it is necessary to process and analyze it for transformation to a value. Firstly, we extract the spots where people visited and stayed from raw geodata. Next, we categorize spots using the spot information to reduce the dimension. Finally, we interpret user preferences by applying a topic model to the matrix of users and the categorized spot. Our results show that we can interpret user preferences as topics from big geospatial data using dimension reduction and a topic model. In addition, we verify a performance of methods that use the topics to predict purchase. We found that the models that use topics outperforms others on the real prediction problem.
Merge-Based Parallel Sparse Matrix-Sparse Vector Multiplication with a Vector Architecture

Sparse matrix-sparse vector multiplication (spMspV) is one of the key linear algebra primitives for various graph algorithms. With sparse input/output vectors, it has superiority in computational efficiency over sparse matrix-dense vector multiplication (spMV). In this paper, we propose a merge-based method called 2DMerge to accelerate spMspV execution on vector architectures. The vector registers are effectively utilized by merging intermediate results in both horizontal and vertical dimensions. In the case of Breadth-first search (BFS) for finding connected components, the evaluation results on large-scale graphs show that our method with a vector architecture achieves 127X and 50X average single-core speedup over spMV and a state-of-the-art spMspV implementation on a X86 architecture. On the same vector architecture, it runs 13X and 9X faster on average than spMV and a baseline spMspV implementation. Compared to a popular computation framework GraphLab on X86, it also realizes 11X average speedup for high-diameter graphs, and 23X for low-diameter graphs with a hybrid strategy. Our method also shows performance superiority in Bellman-Ford single-source-shortest-path (SSSP) algorithm.
Towards a New Approach to Empower Periodic Pattern Mining for Massive Data using Map-Reduce

Recent applications like social networks and IoT are the main source of the massive amount of data generated every day. Time series data is a major form where data is sequenced and indexed by timestamps. Multiple data mining techniques are applied to discover the behavior of time series datasets, periodic pattern mining is one of them. Many sequential pattern mining algorithms were presented, some of them built suffix trees and performed early pruning while other algorithms used pattern-growth techniques such as projection. A few algorithms performed Apriori-based techniques where lattice trees were built and traversed. However, most algorithms suffer from time and space issues when mining large scale time series sequences. In our paper, we present a solution that utilizes advanced and sophisticated distributed systems such as MapReduce framework. It splits the original sequence and distributes its segments across thousands of nodes in the MapReduce infrastructure. We use different training datasets to evaluate both traditional pattern mining algorithms and our MapReduce solution. After analyzing our solution in terms of time complexity, efficiency and accuracy, we clarify the advantages of processing data segments using periodic pattern mining along with MapReduce framework.
A Multi-scale Pyramid of Fully Convolutional Networks for Automatic Cell Detection

Cell detection from the histology images is a crucial prerequisite step to narrow down the interested areas for important following analyses. While detecting cell manually is a time-consuming and labor-intensive work, automatic cell detection is in demand. However, automatic cell detection is a challenging task due to the complexity of histology images, including the variations in cell size, shape and texture. Previous methods favour handcrafted features, which lack the robustness to different cell types, while convolutional neural network based methods often suffer from the problem of maintaining spatial information and semantic information. We propose a novel network structure to leverage the balance between spatial information and semantic information on cell detection. We also design a variant of focal loss to deal with the severe imbalance between nuclei pixels and background pixels, which is a common problem in cell detection that may greatly affect the results from network. Our proposed method is evaluated on a typical colorectal histology image dataset and the result outperforms the previous state-of-the-art method (F1 score 0.834) by a large margin, achieving a new state-of-the-art (F1 score 0.889).
An Indexing Approach for Efficient Supporting of Continuous Spatial Approximate Keyword Queries

Current social-network-based and location-based-service applications need to handle continuous spatial approximate keyword queries over geo-textual streaming data of high density. The state-of-the-art continuous spatial-keyword query indexing approaches generally do not apply for approximate keyword matching. Aiming to this problem, this paper first proposes an indexing approach for efficient supporting of continuous spatial approximate keyword queries by integrating min-wise signatures into AP-tree, namely AP-tree+. AP-tree+ can employ one-permutation min-wise hashing method to support approximate keyword matching with a lower maintenance cost. Towards providing a more efficient indexing approach, this paper has explored the feasibility of parallelizing AP-tree+ by employing Graphic Processing Unit (GPU). A min-wise parallel hashing algorithm is used to parallelize the approximate keyword matching of AP-tree+. The experimental results indicate that (1) AP-tree+ can reduce the space cost by 11.5% compared with MHR-tree, (2) AP-tree+ can hold a comparable recall and 5.64x query performance gain compared with MHR-tree while saving 22.9% maintenance cost on average, and (3) the GPU-aided AP-tree+ can averagely accelerate 5.76x faster than AP-tree+.
Scalable Hardware Trojan Activation by Interleaving Concrete Simulation and Symbolic Execution

Intellectual Property (IP) based System-on-Chip (SoC) design is a widely used practice today. The IPs gathered from third-party vendors may not be trustworthy since they may contain malicious implants (hardware Trojans). To avoid the detection of the Trojan, adversaries usually hide it under rare branches or rare assignments triggered under extremely rare input sequences. Due to exponential input space complexity, state-of-the-art constrained-random test generation methods are not suitable for activating these rare scenarios. While existing model checking based directed test generation approaches are promising, they are not capable of generating tests for large RTL designs due to the capacity restrictions of formal methods. In this paper, we propose an automated and scalable test generation approach for activation of hardware Trojans in RTL designs. This paper makes three important contributions. First, it provides a scalable test generation framework by effective utilization of symbolic execution and concrete simulation. Next, it is a fully automated approach for generating directed tests for activating rare branches and rare assignments. Finally, our experimental results demonstrate that the generated tests are able to activate hard-to-cover Trojans in large and complex RTL benchmarks.
Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease

This paper investigates data for 9 common Alzheimer's Disease risk factors, from three different categories; Medical History, Lifestyle, and Demography. The dataset used consists of 185 normal control, 177 early mild cognitive impairment, 161 late mild cognitive impairment and 127 Alzheimer's Disease subjects. The initial experiment had training results of 0.92 sensitivity, 0.935 specificity and 0.771 precision. However, during the test stage the final output was 0.741 sensitivity, 0.515 specificity and 0.286 precision. The results of this experiment did not give a clear classification or definite predictive value. Involving more variables and underlying data could provide a better outcome. This paper is a part of a long-term study that focuses on the classification and ranking the importance of Alzheimer's Disease risk factors using Machine Learning predictive models and classifications techniques.
Hyper-Heuristics Using Genetic Programming to Time Series Forecasting

Time series forecasting methods allow companies and researchers to analyze and predict data that change over time, such as stock exchange and climate change. However, because of their complexity and dynamic nature, each type of time series ideally should be modeled using ad-hoc algorithms. To create a more general methodology, we proposed a combination of meta-heuristics, led by Genetic Programming (GP), to enhance the overall prediction ability. GP may not be as popular as the Box & Jenkins methodology for forecasting tasks, but the literature shows appealing outcomes. Swarm intelligence is also a powerful mechanism for searching patterns in large data spaces. Thus, we investigated and proposed a hybrid method using GP together with the Fish School Search (FSS) algorithm, where the latter is used to select optimal parameters for the former. We also used local search techniques for preventing the Genetic Programming to get stuck in local minima, by refining the coefficients on the GP expression. Our proposal was compared to standard autoregressive integrated moving average (ARIMA) model, exponential smoothing (ETS) and standard GP. The proposed method achieved promising results in one-step-ahead predictions and was applied to a well-known time series data library.
You Type a Few Words and We Do the Rest: Image Recommendation for Social Multimedia Posts

In this paper, we introduce a new application that can be employed on many social media platforms. We intend to recommend related images from local (e.g. user’s local mobile phone storage) and global (e.g. platform’s server) image pools while a user is composing the text to post a status. To make the recommendation system applicable to different platforms with or without the support of online computing, we propose two independent frameworks that recommend images at image level and data-driven category level based on a text, respectively. For image-level recommendation, our framework recommends images for a text by predicting the affinity scores of image-text pairs and recommending the images with the highest scores for the text. To improve the ranking performance, we propose a novel patch-level image-text matching framework which strikes a balance between local and global matching of image-text pairs. In particular, it first extracts the affinity between each local word and image patch, then leverages different kinds of attention mechanisms to respectively weight the local words and patches for computing the final image-text affinity scores. For category-level recommendation, we first classify images into categories in an unsupervised way, and then propose a multi-task LSTM-based framework with effective user feature for recommendation. Extensive experiments in two real-world social media datasets demonstrate the effectiveness of the proposed models, which significantly outperform the baselines. We also visualize the patch-word matching details to provide an insight into the image-level recommendation framework and demonstrate the strong capacity of category-level recommendation framework to recommend images in an online fashion.
Multi-Label Automatic Labeling for Question Attributes Based on Adaboost and Bayes Algorithms

The attributes, such as knowledge points and difficulty, are important parts of questions. It is related to the automatic classification, auto-generating test paper and adaptive learning. The traditional labeling, however, is done by domain experts and teachers with low efficiency which make it difficult to achieve large-scale automatic labeling. Automatic labeling not only saves a lot of manual labeling costs, but also executes question labeling in batches. Question labeling is an essential text multi-label classification problem. In this paper, we propose an improved multi-label classification algorithm based on naive Bayes algorithm, combined with AdaBoost algorithm. We develop a new evaluation model of classification precision for the hierarchical characteristic of question knowledge point structure. The experimental results demonstrate the proposed algorithm outperforms to the naive Bayes algorithm and the SVM algorithm in precision, recall and Fl- value.