Reviewerly/exHarmony huggingface dataset:
An Optimized Abstractive Text Summarization Model Using Peephole Convolutional LSTM

Abstractive text summarization that generates a summary by paraphrasing a long text remains an open significant problem for natural language processing. In this paper, we present an abstractive text summarization model, multi-layered attentional peephole convolutional LSTM (long short-term memory) (MAPCoL) that automatically generates a summary from a long text. We optimize parameters of MAPCoL using central composite design (CCD) in combination with the response surface methodology (RSM), which gives the highest accuracy in terms of summary generation. We record the accuracy of our model (MAPCoL) on a CNN/DailyMail dataset. We perform a comparative analysis of the accuracy of MAPCoL with that of the state-of-the-art models in different experimental settings. The MAPCoL also outperforms the traditional LSTM-based models in respect of semantic coherence in the output summary.
Grounded language interpretation of robotic commands through structured learning

The presence of robots in everyday life is increasing day by day at a growing pace. Industrial and working environments, health-care assistance in public or domestic areas can benefit from robots' services to accomplish manifold tasks that are difficult and annoying for humans. In such scenarios, Natural Language interactions, enabling collaboration and robot control, are meant to be situated, in the sense that both the user and the robot access and make reference to the environment. Contextual knowledge may thus play a key role in solving inherent ambiguities of grounded language as, for example, the prepositional phrase attachment. In this work, we present a linguistic pipeline for semantic processing of robotic commands, that combines discriminative structured learning, distributional semantics and contextual evidence extracted from the working environment. The final goal is to make the interpretation process of linguistic exchanges depending on physical, cognitive and language-dependent aspects. We present, formalize and discuss an adaptive Spoken Language Understanding chain for robotic commands, that explicitly depends on the operational context during both the learning and processing stages. The resulting framework allows to model heterogeneous information concerning the environment (e.g., positional information about the objects and their properties) and to inject it in the learning process. Empirical results demonstrate a significant contribution of such additional dimensions, achieving up to a 25% of relative error reduction with respect to a pipeline that only exploits linguistic evidence.
Automated vehicle detection in satellite images using deep learning

Automatic detection of small objects such as vehicles in satellite images is a very challenging task, due to the complexity of the background, vehicles colors, the large size of ground sample distance (GSD) for satellite images and jamming caused by buildings and trees. Many methods were proposed for this task by using handcrafted features (such as a Histogram of an Oriented Gradient, Local Binary Pattern, Scale-Invariant Feature Transform, etc.) along with support vector machine classifier, however, Convolutional Neural Networks (CNN) have proved to be potentially more effective. In this paper, we use two advanced deep learning frameworks, Faster Region CNN (Faster R-CNN) and Single Shot Multi-Box (SSD) based on (CNN) with Inception-V2 as a feature map generator instead of VGG-16, to detect vehicles through Transfer Learning, and making an experimental analysis comparison between the two models. Experimental results on the test dataset demonstrate the effectiveness and efficiency of the proposed methods.
A Self-Heuristic Ant-Based Method for Path Planning of Unmanned Aerial Vehicle in Complex 3-D Space With Dense U-Type Obstacles

Optimal path planning is required in autonomous navigation and intelligent control of the unmanned aerial vehicle (UAV). However, as a kind of common obstacles in complex three-dimensional (3-D) spaces, U-type obstacles may cause UAV to be confused and even lead to a collision or out of control. Although most of the Ant Colony Optimization (ACO) algorithm can generate proper path, solutions to U-type obstacles based on the specific behaviors of each ant are investigated rarely. Hence, different search strategies are studied and a novel ACO-based method called Self-Heuristic Ant (SHA) is proposed in this paper. The whole space is constructed by grid workspace model firstly, and then a new optimal function for UAV path planning is built. To avoid ACO deadlock state (i.e., ants are trapped in U-type obstacles when there is no optional successor node), two different search strategies are designed for choosing the next path node. In addition, the SHA is utilized to improve the ability of the basic ACO-based method. Specifically, besides pheromone update, a new information communion mechanism is fused to deal with the special areas which contain dense obstacles or many concave blocks. Finally, several experiments are investigated deeply. The results show that the deadlock state can be reduced effectively by the designed two different search strategies of ants. More importantly, compared with the conventional fallback strategy, the average number of retreats and the average running time of ACO can be reduced when SHA is applied.
EMMA

Establishing trust for an execution environment is an important problem, and practical solutions for it rely on attestation, where an untrusted system (prover) computes a response to a challenge sent by the trusted system (verifier). The response typically is a checksum of the prover's program, which the verifier checks against expected values for a "clean" (trustworthy) system. The main challenge in attestation is that, in addition to checking the response, the verifier also needs to verify the integrity of the response computation. On higher-end processors, this integrity is verified cryptographically, using dedicated trusted hardware. On embedded systems, however, constraints prevent the use of such hardware support. Instead, a popular approach is to use the request-to-response time as a way to establish confidence. However, the overall request-to-response time provides only one coarse-grained measurement from which the integrity of the attestation is to be inferred, and even that is noisy because it includes the network latency and/or variations due to micro-architectural events. Thus, the attestation is vulnerable to attacks where the adversary has tampered with response computation, but the resulting additional computation time is small relative to the overall request-to-response time.
Hand Gesture Recognition System for In-car Device Control Based on Infrared Array Sensor

Nowadays, with the development of automotive industry, more and more functions and devices are assembled in cars to improve driving experience. Meanwhile, traffic accidents are increasing in recent years. Operations of in-car device human machine interface (HMI) will cause lack of concentration, which is a major cause of the accidents. Common in-car device HMI systems are based on optics, acoustics, and so on, which are faced with environment limitations such as influenced by illumination conditions. In order to deal with these limitations, in this paper, an infrared array sensor is applied to construct a hand gesture recognition system for in-car devices control. The proposed system can overcome disadvantages of other systems and has a wider application. In the system, seven different shapes of hand and movement toward four directions are combined to achieve the aim of device operations. In data processing, convolutional neural network (CNN) is applied to realize recognition. Simulated experiments are conducted to verify the feasibility of this system.
Text Generation for Imbalanced Text Classification

The problem of imbalanced data can be frequently found in the real-world data. It leads to the bias of classification models, that is, the models predict most samples as major classes which are often the negative class. In this research, text generation techniques were used to generate synthetic minority class samples to make the text dataset balanced. Two text generation methods: the text generation using Markov Chains and the text generation using Long Short-term Memory (LSTM) networks were applied and compared in the term of ability to improve the performance of imbalanced text classification. Our experimental study is based on LSTM networks classifier. Traditional over-sampling technique was also used as baseline. The study investigated our Thai-language advertisement text dataset from Facebook. According to the increase of recall value, applying of these techniques showed the improvement of an ability to create model predicting more positive samples, which are minority samples. It can be found that the Markov Chains technique outperformed traditional over-sampling and text generation using LSTM in majority of the models.
A Dual Layer Image Encryption using Polymerase Chain Reaction Amplification and DNA Encryption

Unauthorized access of the data is one of the major threat for the real world digital data communication. Digital images are one of the most vital subset of the digital data. Several important and sensitive information is conveyed through digital images. Hence, digital image security is one of the foremost interest of the researchers. Cryptographic algorithms Biological sequences are often used to encrypt data due to their inherent features. DNA encryption is one of the widely used method used for data security which is based on the properties of the biological sequences. To protect the images from unwanted accesses, a new two stage method is proposed in this work. DNA Encryption and Polymerase Chain Reaction (PCR) Amplification is used to enhance the security. The proposed method is evaluated using different standard parameters that shows the efficiency of the algorithm.
Explicit Spatial Encoding for Deep Local Descriptors

We propose a kernelized deep local-patch descriptor based on efficient match kernels of neural network activations. Response of each receptive field is encoded together with its spatial location using explicit feature maps. Two location parametrizations, Cartesian and polar, are used to provide robustness to a different types of canonical patch misalignment. Additionally, we analyze how the conventional architecture, i.e. a fully connected layer attached after the convolutional part, encodes responses in a spatially variant way. In contrary, explicit spatial encoding is used in our descriptor, whose potential applications are not limited to local-patches. We evaluate the descriptor on standard benchmarks. Both versions, encoding 32 × 32 or 64 × 64 patches, consistently outperform all other methods on all benchmarks. The number ofparameters of the model is independent of the input patch resolution.
An Efficient Tree-Based Algorithm for Mining High Average-Utility Itemset

High-utility itemset mining (HUIM), which is an extension of well-known frequent itemset mining (FIM), has become a key topic in recent years. HUIM aims to find a complete set of itemsets having high utilities in a given dataset. High average-utility itemset mining (HAUIM) is a variation of traditional HUIM. HAUIM provides an alternative measurement named the average-utility to discover the itemsets by taking into consideration both of the utility values and lengths of itemsets. HAUIM is important for several application domains, such as, business applications, medical data analysis, mobile commerce, streaming data analysis, etc. In the literature, several algorithms have been proposed by introducing their own upper-bound models and data structures to discover high average utility itemsets (HAUIs) in a given database. However, they require long execution times and large memory consumption to handle the problem. To overcome these limitations, this paper, first, introduces four novel upper-bounds along with pruning strategies and two data structures. Then, it proposes a pattern growth approach called the HAUL-Growth algorithm for efficiently mining of HAUIs using the proposed upper-bounds and data structures. Experimental results show that the proposed HAUL-Growth algorithm significantly outperforms the state-of-the-art dHAUIM and TUB-HAUIM algorithms in terms of execution times, number of join operations, memory consumption, and scalability.
Demonstrating Blockchain-Enabled Peer-to-Peer Energy Trading and Sharing

Blockchain (BC) is becoming a key technology in securing future businesses and economic competition around the world. It is seen as an enabler for trust and security in the growing sharing economy. In this study, we demonstrate the application of BC to energy peer-to-peer (P2P) trading in smart grid. A smart contract for managing trust and transactions is designed and implemented for a use case in energy P2P trading on IBM platform using hyperledger composer. In addition, current challenges that could be faced when implementing BC for securing energy P2P trading are investigated and discussed.
Supporting lower-level processes in EFL listening: the effect on learners’ listening proficiency of a dictation program supported by a mobile instant messaging app

Lower-level processes have long been overlooked in EFL listening pedagogy. To address this pervasive problem, in this study, a program was designed that integrated WeChat, the most popular mobile instant messaging app in China, into dictation practice. Students in two classes studying a compulsory university EFL course were selected as participants, with one class as the experimental group (EG, n = 37) and the other as the control group (CG, n = 33). Both groups attended the same weekly in-class listening course taught by the same instructor, in which traditional listening exercises were combined with explicit decoding instructions. Members of the EG were asked to participate in a WeChat group of 8–10 students to complete daily dictation, while the CG did the practice individually. A listening test served as pre- and post-test, and an open-question survey was used to collect the EG's perceptions of the WeChat dictation program. The results show that the EG's improvement in listening was significantly greater than that of the CG. There were no differences in improvement in participants' listening comprehension between males and female, whereas listening gains differed significantly between the EG and the CG when participants were at different initial listening levels. The students' perceptions of the benefits and drawbacks of the program are discussed.
Consensus clustering algorithm based on the automatic partitioning similarity graph

Consensus clustering has been recently applied as a solution to the clustering problem. This combines multiple clusterings of a set of objects into a single integrated clustering. Consensus clustering algorithms attempt to find stable and robust results by composing calculated results from the base clustering algorithms. However, finding a consensus clustering algorithm capable of obtaining the final clusters automatically has remained a challenge. Furthermore, most of them are affected by an outlier. In the present paper, a cluster-based consensus clustering algorithm is proposed based on partitioning similarity graph in which each vertex is a cluster composed of a set of points. Within the proposed algorithm, the Cosine, Jaccard, and Dice similarity measures are used to measure the similarity between two vertices. The proposed algorithm operates in three steps. First, outlier clusters are automatically obtained by pruning similarity graph. Then, meta-clusters are obtained by splitting graph and merging sub-graphs. Finally, based on the meta-clusters, the consensus solution and outlier points are obtained using majority voting. The proposed algorithm has linear time complexity in terms of the number of points. The number of clusters also is obtained automatically in a consensus solution. To evaluate the performance of the algorithm, real and artificial datasets were used. The obtained results showed a dramatic improvement in the accuracy of the final clusters.
Optimal Few-Weight Codes From Simplicial Complexes

Recently, some infinite families of binary minimal and optimal linear codes were constructed from simplicial complexes by Hyun et al. Inspired by their work, we present two new constructions of codes over the ring F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> by employing simplicial complexes. When the simplicial complexes are all generated by a maximal element, we determine the Lee weight distributions of two classes of the codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Our results show that the codes have few Lee weights. Via the Gray map, we obtain an infinite family of binary codes meeting the Griesmer bound and a class of binary distance optimal codes.
Investigation of users' knowledge change process in learning‐related search tasks

ABSTRACT This study aims to examine users' knowledge change characteristics and knowledge change process, and examined factors that may influence users' knowledge change process. A user search experiment was conducted, in which participants were asked to search for two learning‐related search tasks and use a mind‐map to organize their thoughts during the search process. Pre‐ and post‐search questionnaires were distributed to collect users' self‐assessed topic familiarity and task difficulty. Besides the overall knowledge change process by all the users, we identified four types of knowledge change styles using a hierarchical clustering method: Early knowledge change, Medium‐term knowledge change, Late knowledge change and Average knowledge change. In addition, we found that task type, task difficulty in information integration and difficulty in drawing the mind‐map had significant influences on users' knowledge change styles. This study took a process perspective to characterize users' knowledge change at different stages and shed light on when and how learning occurs during the search process.
Biologically Plausible Fuzzy-Knowledge-Out and Its Induced Wide Learning of Interpretable TSK Fuzzy Classifiers

As an alternative to existing construction methods of Takagi-Sugeno-Kang (TSK) fuzzy classifiers, this paper presents a novel design methodology formulated by a new concept called fuzzy-knowledge-out and its induced wide learning way. Analogous to the “dropout” concept in deep learning, the concept of fuzzy-knowledge-out in TSK fuzzy classifiers is motivated by the firing pattern of knowledge in biological neural networks. Our theoretical analysis reveals that a fuzzy classifier built after fuzzy-knowledge-out from a complete set of highly interpretable fuzzy rules is distinctive in generalization and coadaption avoidance. As such, an ensemble called wide learning based TSK (WL-TSK), of highly interpretable zero-order TSK fuzzy subclassifiers constructed quickly by means of fuzzy-knowledge-out operations in a wide learning manner is proposed to achieve enhanced classification performance and high interpretability. With the use of the proposed halving or averaging operations, WL-TSK essentially behaves like only one zero-order TSK fuzzy classifier. Thus, the proposed method can be considered as a new design methodology of TSK fuzzy classifiers. Our experimental results on 15 datasets indicate the effectiveness of WL-TSK in terms of both enhanced classification performance and high interpretability.
EPAN: Effective parts attention network for scene text recognition

For most previous attention-based scene text recognition methods, images are transformed into high-level feature vectors that form a feature map with height equal to one. Such vectors may contain unnecessary noise that limits recognition performance. To address this issue, in this paper, we propose the effective parts attention network (EPAN) which can attentively highlight the character region for more precise recognition. EPAN consists of a text image encoder and character effective parts decoder (CEPD), and it is end-to-end trainable. The former separates the high-dimensional feature map into one-dimensional vectors row-by-row, which are connected to a bidirectional long short term memory unit to encode contextual information. Subsequently, the CEPD transforms the vectors using a novel glimpse network at each time step to roughly determine the position of the characters. Then the CEPD uses a refinement network to generate a mask to gradually localize the precise position of important parts of the current character. Experiments were conducted on various benchmarks, including IIIT5K-Words, Street View Text, ICDAR 2003, ICDAR 2013, CUTE80, Street View Text Perspective, and ICDAR 2015, which demonstrated that the proposed EPAN method significantly outperformed or was comparable to existing methods in terms of lexicon-free word accuracy. Additionally, substantial qualitative results further demonstrated the robustness of our method.
Crowdsourced Wireless Spectrum Anomaly Detection

Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming impossible because of the large number of measurement locations and complexity of the spectrum use landscape. Detecting unexpected behaviors in the wireless spectrum from the collected data is a crucial part of this automated monitoring, and the control of detected anomalies is a key functionality to enable interaction between the automated system and the end user. In this paper we look into the wireless spectrum anomaly detection problem for crowdsourced sensors. We first analyze in detail the nature of these anomalies and design effective algorithms to bring the higher dimensional input data to a common feature space across sensors. Anomalies can then be detected as outliers in this feature space. In addition, we investigate the importance of user feedback in the anomaly detection process to improve the performance of unsupervised anomaly detection. Furthermore, schemes for generalizing user feedback across sensors are also developed to close the anomaly detection loop.
A Worst-Case Optimal Join Algorithm for SPARQL

Worst-case optimal multiway join algorithms have recently gained a lot of attention in the database literature. These algorithms not only offer strong theoretical guarantees of efficiency, but have also been empirically demonstrated to significantly improve query runtimes for relational and graph databases. Despite these promising theoretical and practical results, however, the Semantic Web community has yet to adopt such techniques; to the best of our knowledge, no native RDF database currently supports such join algorithms, where in this paper we demonstrate that this should change. We propose a novel procedure for evaluating SPARQL queries based on an existing worst-case join algorithm called Leapfrog Triejoin. We propose an adaptation of this algorithm for evaluating SPARQL queries, and implement it in Apache Jena. We then present experiments over the Berlin and WatDiv SPARQL benchmarks, and a novel benchmark that we propose based on Wikidata that is designed to provide insights into join performance for a more diverse set of basic graph patterns. Our results show that with this new join algorithm, Apache Jena often runs orders of magnitude faster than the base version and two other SPARQL engines: Virtuoso and Blazegraph.