Reviewerly/exHarmony huggingface dataset:
Hybrid ant colony optimization model for image retrieval using scale-invariant feature transform local descriptor

An organization uses a symbol as its representation in the market for ease of identification and uniqueness. Logos are used to identify and retrieve the materials, even in a complex environment for further analysis. Algorithms based on support vector machine and neural networks provide better results in retrieval of the document from small dataset. But inlarge data sets the existing models lags in their classification performance. This proposed model uses ant colony optimization (ACO) along with the local descriptor scale-invariant feature transform (SIFT), as a hybrid model for retrieving document from dataset. This hybrid model enhances the performance of the retrieval model in terms of increased efficiency, leading to an accuracy of 95.86% with a high output precision of 97.67%.
Is Arbitrage Possible in the Bitcoin Market? (Work-In-Progress Paper)

Bitcoin is a digital currency traded on different exchanges for different prices; this feature implies important issues about arbitrage opportunities. In this paper we investigate whether strong or weak form of arbitrage strategies are indeed possible by trading across different Bitcoin Exchanges. Our investigation, both theoretically and practically, gives as a result that arbitrage is indeed possible.
Towards Cycle-Consistent Models for Text and Image Retrieval

Cross-modal retrieval has been recently becoming an hot-spot research, thanks to the development of deeply-learnable architectures. Such architectures generally learn a joint multi-modal embedding space in which text and images could be projected and compared. Here we investigate a different approach, and reformulate the problem of cross-modal retrieval as that of learning a translation between the textual and visual domain. In particular, we propose an end-to-end trainable model which can translate text into image features and vice versa, and regularizes this mapping with a cycle-consistency criterion. Preliminary experimental evaluations show promising results with respect to ordinary visual-semantic models.
Combining different metadata views for better recommendation accuracy

Recommender systems emerged as means to help users deal with information overload by filtering content based on their preferences. Regardless of the recommendation method, there has been a recent interest in using user reviews as source of information, since they contain both detailed items’ descriptions as well as users’ opinions. Even though several works have been done in the subject, very few of them consider different views that the items may have towards their features, selecting only a method of weighting their features. In this work, we propose a system that combines two item representations that represent different views of the same feature set: one based on its statistics and the other based on its quality. Features are disambiguated concepts extracted from users’ reviews. We propose several strategies divided into three categories: pre combination, neighborhood combination and post combination. We evaluate our strategies in two data sets, comparing them with each other and against the isolated item representations, as well as a representation baseline based on terms and sentiment analysis. Results are promising showing that some combinations are capable of producing better rankings than their isolated versions.
Obtaining fault tolerance avoidance behavior using deep reinforcement learning

In this article, a mapless movement policy for mobile agents, designed specifically to be fault-tolerant, is presented. The provided policy, which is learned using deep reinforcement learning, has advantages compared to the usual mapless policies: this policy is capable of handling a robot even when some of its sensors are broken. It is an end-to-end policy based on three neuronal models capable not only of moving the robot and maximizing the coverage of the environment but also of learning the best movement behavior to adapt it to its perception needs. A custom robot, for which none of the readings of the sensors overlap each other, has been used. This setup makes it possible to determine the operation of a robust failure policy, since the failure of a sensor unequivocally affects the perceptions. The proposed system exhibits several advantages in terms of robustness, extensibility and utility. The system has been trained and tested exhaustively in a simulator, obtaining very good results. It has also been transferred to real robots, verifying the generalization and the good functioning of our model in real environments.
Unsupervised Joint PoS Tagging and Stemming for Agglutinative Languages

The number of possible word forms is theoretically infinite in agglutinative languages. This brings up the out-of-vocabulary (OOV) issue for part-of-speech (PoS) tagging in agglutinative languages. Since inflectional morphology does not change the PoS tag of a word, we propose to learn stems along with PoS tags simultaneously. Therefore, we aim to overcome the sparsity problem by reducing word forms into their stems. We adopt a Bayesian model that is fully unsupervised. We build a Hidden Markov Model for PoS tagging where the stems are emitted through hidden states. Several versions of the model are introduced in order to observe the effects of different dependencies throughout the corpus, such as the dependency between stems and PoS tags or between PoS tags and affixes. Additionally, we use neural word embeddings to estimate the semantic similarity between the word form and stem. We use the semantic similarity as prior information to discover the actual stem of a word since inflection does not change the meaning of a word. We compare our models with other unsupervised stemming and PoS tagging models on Turkish, Hungarian, Finnish, Basque, and English. The results show that a joint model for PoS tagging and stemming improves on an independent PoS tagger and stemmer in agglutinative languages.
Single MR image super-resolution via channel splitting and serial fusion network

In magnetic resonance imaging (MRI), spatial resolution is an important and critical imaging parameter that represents how much information is contained in a unit space. Acquiring high-resolution MRI data usually takes a long scanning time and is subject to motion artifacts due to hardware, physical, and physiological limitations. Single image super-resolution (SISR) based on deep learning is an effective and promising alternative technique to improve the native spatial resolution of magnetic resonance (MR) images. However, because of the simple diversity and single distribution of training samples, the effective training of deep models with medical training samples and improvement of the tradeoff between model performance and computing overhead are major challenges. In addition, deeper networks are more difficult to effectively train since the information is gradually weakened as the network deepens. In this paper, a novel channel splitting and serial fusion network (CSSFN) is presented for single MR image super-resolution. The proposed CSSFN splits hierarchical features into a series of subfeatures, which are then integrated together in a serial manner. Hence, the network becomes deeper and can discriminatively and reasonably deal with the subfeatures. Moreover, a dense global feature fusion (DGFF) is adopted to integrate the intermediate features, which further promotes the information flow in the network and helps to stabilize model training. Extensive experiments on several typical MR images show the superiority of our CSSFN models to other advanced SISR methods.
Microblog summarization using Paragraph Vector and semantic structure

There are two fundamental difficulties that are still hindering the development of microblog summarization. The first problem is the features sparseness of microblog, which restricts the performance of sub-topics detection. The second one is the sentence selection from sub-topics that is based mainly on centrality approaches to measure sentence salience. Also, the semantic features and relations features between sentences and sub-topics were not given much attention. In order to address the two aforementioned problems, we propose a summarization method considering Paragraph Vector and semantic structure. Firstly, we construct sentence similarity matrix that involves the contextual information of microblogs to detect sub-topics by using Paragraph Vector. Secondly, we analyze the sentences by utilizing Chinese Sentential Semantic Model (CSM) to get semantic features; then the relations features are obtained based on the similarity matrix and semantic features above. Finally, the most informative sentences can be selected accurately from microblogs belonging to the same sub-topics by semantic features and relation features. The experimental results show that the ROUGE-1 value is up to 53.17% with 1.5% compression ratio. The results indicate that applying Paragraph Vector to the field of microblog summarization can effectively improve sub-topics detection. Additionally, semantic features and relation features enhance summarization result jointly. Furthermore, CSM provides a promising tool for sentence semantic analysis.
Deep Spatiotemporal Relation Learning With 3D Multi-Level Dense Fusion for Video Action Recognition

Two-stream Convolutional Neural Network has shown a remarkable performance for video action recognition. Many recent works mainly focus on the fusion of appearance and motion information to obtain a robust spatiotemporal representation for action video. However, most of these networks are based on 2D convolution architecture and apply spatiotemporal fusion at the top layers of the network, which lacks the capability to take full advantage of the potential spatiotemporal relation in multiple levels of the network as well as capture the temporal dynamic in low-level details. In this paper, we propose a novel convolutional fusion network based on a two-stream network, called 3D Multi-Level Dense Fusion (MLDF-3D) for the deep spatiotemporal relation learning. There are mainly three merits of the proposed network: (i) rather than performing fusion only at the last convolution layer or softmax layer, the MLDF-3D performs spatiotemporal fusion at multiple levels of network, which can fully explore the potential relation of features extracted from two-stream network; (ii) we introduce dense connection in MLDF-3D to make fusion features at multiple levels directly connected; and (iii) we develop a sequence segment framework for the long-range temporal structure modeling. Our experimental results show that the MLDF-3D can effectively learn the correlation of two-stream features to obtain robust action representation, and achieve the state-of-the-art performance on HMDB51 and UCF101.
Toward multi-lingual information retrieval system based on internet linguistic diversity measurement

We introduce a method for measuring the quantity of online content of a set of languages at domain level. This measurement is used for building a Multi-Lingual Information Retrieval (MLIR) system that identifies which languages are strongly represented on the internet about a specific query topic. The system architecture includes two modules; the off-line module builds a linguistic diversity index for languages at topic level and the on-line module, where the suitable language for search is identified based the index for retrieving the relevant documents to the user query in that language. The conducted experiments explore the usefulness of building such an index and its usage effect on both of monolingual and traditional MLIR system. From the obtained results, it has been proven that the more internet resources, the better the accuracy of the retrieved results, and therefore the better the system performance.
Multi-level and multi-scale deep saliency network for salient object detection

Traditional saliency model usually utilize handcrafted image features and various prior knowledge to pop out salient regions from complex surroundings. In this paper, we propose a novel FCN-like deep convolutional neural network for pixel-wise salient object detection. Our deep network automatically learns multi-level feature from different convolutional layers of a pre-trained convolutional neural network. Moreover, deeper side outputs are connected to the shallower ones, which provides a better feature representation and helps shallow side outputs to accurately locate salient regions. In addition, we adopt a weighted-fusion module to combine different side outputs for utilizing multi-scale and multi-level features. Finally, a fully connected CRF model can be optimally incorporated to improve spatial coherence and contour localization in the fused saliency map. Both qualitative and quantitative evaluations on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 17 state-of-the-art methods.
Securing a communication channel for the trusted execution environment

As a security extension to processor, ARM TrustZone has been widely adopted for various mobile and IoT devices. The protection is conducted by separating the system into two domains: the rich execution environment (REE) and the trusted execution environment (TEE). Although the TEE effectively isolates the critical resources based on hardware access control technologies, the communication channel between the REE and the TEE has been regarded as vulnerable and exploited by attackers to deliver malicious messages to the TEE, which undermines the entire TEE security. SeCReT (NDSS 15) introduced the first solution to protect the communication channel. Unfortunately, this method has several challenges associated with it, making it difficult to deploy the solution in production devices. This study illustrates such challenges in terms of performance and security. In addition, a design optimization of the initial version of SeCReT is proposed to mitigate these challenges and evaluated to highlight its effectiveness.
Multiple Wavelet Pooling for CNNs

Pooling layers are an essential part of any Convolutional Neural Network. The most popular pooling methods, as max pooling or average pooling, are based on a neighborhood approach that can be too simple and easily introduce visual distortion. To tackle these problems, recently a pooling method based on Haar wavelet transform was proposed. Following the same line of research, in this work, we explore the use of more sophisticated wavelet transforms (Coiflet, Daubechies) to perform the pooling. Additionally, considering that wavelets work similarly to filters, we propose a new pooling method for Convolutional Neural Network that combines multiple wavelet transforms. The results achieved demonstrate the benefits of our approach, improving the performance on different public object recognition datasets.
Personalized Online Spell Correction for Personal Search

Spell correction is a must-have feature for any modern search engine in applications such as web or e-commerce search. Typical spell correction solutions used in production systems consist of large indexed lookup tables based on a global model trained across many users over a large scale web corpus or a query log.
The use of assay data as a foundation for a geometallurgical model—The case of the Thaba Chromite Mine, South Africa

The Lower and Middle Group chromitites of the Bushveld Igneous Complex in South Africa are the source of a very large portion of the global chrome supply. The recovery of platinum group elements and base metals (Ni, Cu) as by-products has the potential to add value to these chrome resources. Yet, the effectiveness of chromite and platinum-group element beneficiation circuits is highly sensitive to variations in feed composition. Of particular relevance is the abundance of alteration silicates, which have a negative impact on recoveries and concentrate grade. The use of geochemical proxies, based on data acquired routinely during the exploration and mining process may provide a cost- and time-efficient alternative to more time-consuming and expensive mineralogical analyses. Such an approach is presented in this study, which focuses on the LG-6, LG-6A, MG-1 and MG-2 chromitite seams at the Thaba mine located on the western limb of the Bushveld Complex. An extensive assay data set comprising of data for Cr2O3, FeO, Al2O3, MgO, SiO2, CaO and P provided by the mine owner was evaluated and corrected for systematic bias between different sample batches. A statistical assessment was performed to discuss the variability within and between the chromitite seams and to separate the mine lease area into distinct geochemical clusters. The distribution of the samples belonging to the different geochemical clusters was then transposed onto the geology of the mine lease area. This allowed the definition of spatial domains. These spatial domains, recognized by the assessment of assay data only, are then validated by mineralogical attributes; implications for mineral beneficiation are critically assessed. According to this assessment, the chromitites of the Thaba mine area can be subdivided into three distinct domains, domains that constitute the suitable fundament for a geometallurgical model. An extensive supergene altered domain is distinguished from a domain affected by hydrothermal alteration. The latter domain occurs below the depth of modern weathering, but in obvious proximity to faults and around a prominent dunite pipe. The third domain is represented by ores least affected by post-magmatic alteration processes. This domain occupies the centre of fault blocks below the extent of modern weathering.
Efficient and simple scalable image compression algorithms

Most wavelet-based scalable image compression algorithms are optimized for performance only, i.e., they try to produce a compressed bit-stream that gives the best image quality for the selected bit-rate. Unfortunately, the optimization of the coding method may result in a bit-stream that cannot be rearranged easily to support resolution scalability. This paper proposes two algorithms. The first algorithm produces a rate scalable bit-stream that is optimized for performance and scalability. That is, it has good performance and at the same time it produces bit-stream that can be easily rearranged to become highly (rate and resolution) scalable. To this end, the algorithm combines the low complexity and rate scalability features of the set partitioning coding approach, and it exploits the resolution scalability characteristics of the block-based systems. The second algorithm upgrades the first algorithm to produce a highly scalable bit-stream by only arranging and identifying the different resolutions within the bit-stream.
A novel word ranking method based on distorted entropy

This paper proposes an application of distorted entropy as well-known tools for non-additive expected utility theory in word ranking. Our algorithms for two books “Statistical Inference” by Casella and Berger and “The Origin of Species” by Charles Darwin show that our method on the distorted entropy improves the corresponding ones in the literature.
A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide Images in Histopathology

Stain normalization is one of the main tasks in the processing pipeline of computer-aided diagnosis systems in modern digital pathology. Some of the challenges in this tasks are memory and runtime bottlenecks associated with large image datasets. In this work, we present a scalable and fast pipeline for stain normalization using a state-of-the-art unsupervised method based on stain-vector estimation. The proposed system supports single-node and distributed implementations. Based on a highly-optimized engine, our architecture enables high-speed and large-scale processing of high-magnification whole-slide images (WSI). We demonstrate the performance of the system using measurements from different datasets. Moreover, by using a novel pixel-sampling optimization we show lower processing time per image than the scanning time of ultrafast WSI scanners with the single-node implementation and additional 3.44 average speed-up with the 4-nodes distributed pipeline.
Design and evaluation of a time adaptive multimodal virtual keyboard

The usability of virtual keyboard based eye-typing systems is currently limited due to the lack of adaptive and user-centered approaches leading to low text entry rate and the need for frequent recalibration. In this work, we propose a set of methods for the dwell time adaptation in asynchronous mode and trial period in synchronous mode for gaze based virtual keyboards. The rules take into account commands that allow corrections in the application, and it has been tested on a newly developed virtual keyboard for a structurally complex language by using a two-stage tree-based character selection arrangement. We propose several dwell-based and dwell-free mechanisms with the multimodal access facility wherein the search of a target item is achieved through gaze detection and the selection can happen via the use of a dwell time, soft-switch, or gesture detection using surface electromyography in asynchronous mode; while in the synchronous mode, both the search and selection may be performed with just the eye-tracker. The system performance is evaluated in terms of text entry rate and information transfer rate with 20 different experimental conditions. The proposed strategy for adapting the parameters over time has shown a significant improvement (more than 40%) over non-adaptive approaches for new users. The multimodal dwell-free mechanism using a combination of eye-tracking and soft-switch provides better performance than adaptive methods with eye-tracking only. The overall system receives an excellent grade on adjective rating scale using the system usability scale and a low weighted rating on the NASA task load index, demonstrating the user-centered focus of the system.
Surface shape determination with a stitching Michelson interferometer and accuracy evaluation

Stitching methods are increasingly used for determining the surface shape of large and high precision optical elements used in synchrotron beamlines. They consist in reconstructing the surface topography from multiple measurements on overlapping parts of the optics aperture by various algorithms. This paper is an attempt to investigate how true and accurate such a reconstruction can be. Error sources are identified and evaluated throughout the acquisition and processing steps. The analysis is based on the example SOLEIL Michelson interferometer for nano-topography, a dedicated measurement bench for stitching interferometry. We propose a method for determining the error made on the estimate of the interferometric reference surface from the stitching dataset. This determination is made before and independently of the stitching procedure itself.
Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy.