Reviewerly/exHarmony huggingface dataset:
A Hybrid Hierarchical Heuristic-ACO With Local Search Applied to Travelling Salesman Problem, AS-FA-Ls

The combinatorial optimization problem is attracting research because they have a wide variety of applications ranging from route planning and supply chain optimization to industrial scheduling and the IoT. Solving such problems using heuristics and bio-inspired techniques is an alternative to exact solutions offering acceptable solutions at fair computational costs. In this article, a new hierarchical hybrid method is proposed as a hybridization of Ant Colony Optimization (ACO), Firefly Algorithm (FA), and local search (AS-FA-Ls). The proposed methods are compared to similar techniques on the traveling salesman problem, (TSP). ACO is used in a hierarchical collaboration schema together with FA which is used to adapt ACO parameters. A local search strategy is used which is the 2 option method to avoid suboptimal solutions. A comparative review and experimental investigations are conducted using the TSP benchmarks. The results showed that AS-FA-Ls returned better results than the listed works in the following cases: berlin52, st70, eil76, rat99, kroA100, and kroA200. Computational investigations allowed determining a set of recommended parameters to be used with ACO for the TSP instances of the study.
One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble

The task of grapheme-to-phoneme (G2P) conversion is important for both speech recognition and synthesis.Similar to other speech and language processing tasks, in a scenario where only small-sized training data are available, learning G2P models is challenging.We describe a simple approach of exploiting model ensembles, based on multilingual Transformers and self-training, to develop a highly effective G2P solution for 15 languages.Our models are developed as part of our participation in the SIGMORPHON 2020 Shared Task 1 focused at G2P.Our best models achieve 14.99 word error rate (WER) and 3.30 phoneme error rate (PER), a sizeable improvement over the shared task competitive baselines.
Persistent Fault Analysis with Few Encryptions

Persistent fault analysis (PFA) consists in guessing block cipher secret keys by biasing their substitution box. This paper improves the original attack of Zhang et al. on AES-128 presented at CHES 2018. By a thorough analysis, the exact probability distribution of the ciphertext (under a uniformly distributed plaintext) is derived, and the maximum likelihood key recovery estimator is computed exactly. Its expression is turned into an attack algorithm, which is shown to be twice more efficient in terms of number of required encryptions than the original attack of Zhang et al. This algorithm is also optimized from a computational complexity standpoint. In addition, our optimal attack is naturally amenable to key enumeration, which expedites full 16-bytes key extraction. Various tradeoffs between data and computational complexities are investigated.
Single Image Super-Resolution Based on Self-Attention

Single image super-resolution (SISR) is a challenging task and has collected extensive attention in both industry and academia. The most challenging problem in super-resolution is how to recover the finer texture details. And we find the generated images usually have low-scale contrast. In this paper, we present a novel super-resolution method based on generative adversarial networks (GAN). Our model is based on SRGAN, instead, we remove most of the batch normalization layers in generator to get higher-scale contrast images and accelerate training speed. Because batch normalization layers can get rid of range flexibility from networks, this causes the generated images have lower-scale contrast compared to the origin images. We also add the self-attention module into generator to get more global dependencies when convolution operations can capture enough local dependencies but little global dependencies. We take advantage of both local dependencies and global dependencies to improve super-resolved texture details and structural, we call our model SASRGAN. The images generated by our model have higher Structural Similarity Index Measure (SSIM), it proves that our model has available performance.
Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

There is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao.com. Current video production workflow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e.g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging film-making principles to improve viewing experience and perceived persuasiveness.
On the Choice of Auxiliary Languages for Improved Sequence Tagging

Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models.In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language distances and show that the most related language is not always the best auxiliary language.Further, we show that attention-based meta-embeddings can effectively combine pre-trained embeddings from different languages for sequence tagging and set new state-of-the-art results for part-of-speech tagging in five languages.
NNC β-OPEN SETS

Adv. Math. Sci. J. Vol. 9, No. 4 Open Access NNC β-OPEN SETS A. Vadivel and C. J. Sundar DOI: https://doi.org/10.37418/amsj.9.4.82 Full Text Abstract Abstract In this article, we study a new types of *** QuickLaTeX cannot compile formula: beta *** Error message: Cannot connect to QuickLaTeX server: cURL error 60: SSL certificate problem: certificate has expired Please make sure your server/PHP settings allow HTTP requests to external resources ("allow_url_fopen", etc.) These links might help in finding solution: http://wordpress.org/extend/plugins/core-control/ http://wordpress.org/support/topic/an-unexpected-http-error-occurred-during-the-api-request-on-wordpress-3?replies=37 -open sets and *** … page AMSJ9-N4-82 Read More »
Visual-attention gabor filter based online multi-armored target tracking

The multi-armored target tracking (MATT) plays a crucial role in coordinated tracking and strike. The occlusion and insertion among targets and target scale variation is the key problems in MATT. Most state-of-the-art multi-object tracking (MOT) works adopt the tracking-by-detection strategy, which rely on compute-intensive sliding window or anchoring scheme in detection module and neglect the target scale variation in tracking module. In this work, we proposed a more efficient and effective spatial-temporal attention scheme to track multi-armored target in the ground battlefield. By simulating the structure of the retina, a novel visual-attention Gabor filter branch is proposed to enhance detection. By introducing temporal information, some online learned target-specific Convolutional Neural Networks (CNNs) are adopted to address occlusion. More importantly, we built a MOT dataset for armored targets, called Armored Target Tracking dataset (ATTD), based on which several comparable experiments with state-of-the-art methods are conducted. Experimental results show that the proposed method achieves outstanding tracking performance and meets the actual application requirements.
Forensic investigations of popular ephemeral messaging applications on Android and iOS platforms

Ephemeral messaging applications are growing increasingly popular on the digital mobile market. However, they are not always used with good intentions. Criminals may see a gateway into private communication with each other through this transient application data. This could negatively impact criminal court cases for evidence, or civil matters. To find out if messages from such applications can indeed be recovered or not, a forensic examination of the device would be required by the law enforcement authority. This paper reports mobile forensic investigations of ephemeral data from a widerange of applications using both proprietary and freeware forensic tools. Both Android and iOS platforms were used in the investigation. The results from the investigation uncovered various artefacts from the iOS device including account information, contacts, and evidence of communication between users. The Android device uncovered evidence of communications, and several media files assumed to be deleted within a storage cache in the Android file system. The forensic tools used within the investigations were evaluated using parameters from the National Institute of Standards and Technology’s (NIST) mobile tool test assertions and test plan.
The Knowledge Economy and Open Innovation: Evidence from Pakistan

In the era of industrialization, the role of small and mediumsized enterprises (SMEs) is more crucial to boost the economy of every nation.Particularly, in line with developed nations, SMEs has also key importance for developing nations like Pakistan.However, the SME's performance in open innovation (OI) is not reached the satisfactory position in Pakistan.That is the reason, the Pakistani SMEs are not achieved the higher performance.In response to this issue, the objective of this study is to investigate the role of maximization of internal innovation and external knowledge incorporation to increase the firm's OI performance.Furthermore, the moderating role of knowledge management is also observed.Managerial staff of SMEs was preferred to get response.After evaluating the data by using Smart PLS 3, it was found that maximization of internal innovation; external knowledge incorporation and knowledge management is the key contributor to enhance OI.Additionally, knowledge management is one of the moderating factors which enhance the positive effect of internal innovation and knowledge management on OI.Thus, the study is significant for SMEs and practitioners to enhance OI performance in SMEs.
Integrated Process Model for Systems Product Line Engineering of Physical Protection Systems

A physical protection system (PPS) integrates people, procedures, and equipment for the protection of assets or facilities against theft, sabotage, or other malevolent intruder attacks. Designing effective PPSs is not trivial and requires the consideration of multiple different concerns. Hence, several PPS methods have been proposed in the literature to design and analyze PPSs to realize the envisioned objectives. These methods have mainly considered the design of a single PPS. Yet, despite the differences, PPSs also share a common design and set of features and likewise can be developed using a systematic large scale reuse approach. Product line engineering (PLE) has been used in various application domains to exploit the potential for large scale reuse, and with this reduce the time-to-market, reduce the cost, and the overall quality of the developed systems. In this paper, we first report on the results of our study to explicitly model the process for developing PPSs. Subsequently, we present the integration of the PPS method with the current PLE method. For modeling the processes, we adopt the Business Process Modeling Notation (BPMN). The resulting method can be applied to the development of various PPSs while considering large-scale reuse.
A healthcare‐oriented mobile question‐and‐answering system for smart cities

Abstract Question‐and‐answering (QA) systems are popularly applied and deployed in many fields and industries, such as e‐commerce platforms, government departments, and educational industry. However, the requirement for QA systems in healthcare industry has still not been satisfied, especially the requirement for mobile QA systems. In this article, we develop a healthcare‐oriented mobile QA system for smart cities. The developed system is constructed with artificial intelligence and mobile computing techniques, including natural language processing, information retrieval, and service computing. To meet the strict requirement of healthcare‐oriented information systems, we design a series of models, algorithms, and computation methods. The designed QA system contains three modules, which are classifier, QA engine, and chatbots API. We study the performance of different classifiers, including neural network‐based classifier, support vector machine (SVM), and AdaBoost‐based classifier. The QA engine consists of two submodules, that is, semantic processing and answers retrieval. Semantic processing contains part‐of‐speech tagging and dependency parsing. The answers retrieval module contains index building and searching. We perform a series of experiments to evaluate the performance of our system and present the experimental results. The built mobile QA system has been applied in real‐world hospitals and communities and receives satisfied user experience.
An FPGA-Based Hardware Accelerator for Real-Time Block-Matching and 3D Filtering

Block-matching and 3D filtering (BM3D) denoising algorithm has been employed in many application fields because of its superior image processing quality. Due to the huge computational workload, real-time implementation of this algorithm is very challenging. Recently, studies on accelerating the BM3D algorithm on GPU have presented impressive speed up over CPU-based implementations. However, GPU devices are generally inefficient in energy dissipation and, thus, are not suitable for embedded application scenarios. In this paper, we propose a dedicated hardware accelerator design to efficiently boost the BM3D algorithm with reduced power consumption on FPGA device. The proposed design is based on a deeply pipelined OpenCL kernel architecture that can efficiently speed up the compute-intensive procedures of the denoising algorithm by exploiting the intrinsic parallelism and maximizing data reuse. The final design was implemented on Intel's Arria-10 GX1150 FPGA, and achieved an average 1.2× performance boost and an outstanding 8.3× reduction in energy dissipation when compared to a state-of-the-art GPU-based software design.
Interaction Between Consumers and Emerging forms of Artificial Intelligence: A Discriminant Analysis

Abstract The interaction between consumers and companies has been changed because of the development and implementation of artificial intelligence. On one hand, the implementation of artificial intelligence systems increases the efficiency and rapidity of certain processes, by making the life of consumers and companies easier. On the other hand, their implementation brings certain challenges because of the changes it involves, including the acceptance of artificial intelligence systems by the consumers, the ability to learn how to operate the robots as well as the protection of the information gathered by these systems. In this paper, we aim to measure the acceptance of consumers regarding different forms of artificial intelligence systems. By applying a discriminant analysis, we measure the preference of consumers towards human versus robot interaction as well as between different types of robots with different forms and degrees of anthropomorphic characteristics. The results show that consumers prefer human interaction to the interaction with robots, especially in cases where they are not familiar with the robot interaction. Besides, they prefer the communication to classic robots in comparison to human holograms and they have a certain curiosity towards humanoid robots in opposition to classic robots.
Biogeography-Based Optimization for Weight Optimization in Elman Neural Network Compared with Meta-Heuristics Methods

In this paper, we present a learning algorithm for the Elman Recurrent Neural Network (ERNN) based on Biogeography-Based Optimization (BBO).The proposed algorithm computes the weights, initials inputs of the context units and self-feedback coefficient of the Elman network.The method applied for four benchmark problems: Mackey Glass and Lorentz equations, which produce chaotic time series, and to real life classification; iris and Breast Cancer datasets.Numerical experimental results show improvement of the performance of the proposed algorithm in terms of accuracy and MSE eror over many heuristic algorithms.
Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets

In the last years, the control of online user generated content is becoming a priority, because of the increase of online aggressiveness and hate speech legal cases.Considering the complexity and the importance of this issue, this paper presents an approach that combines the deep learning framework with linguistic features for the recognition of aggressiveness in Mexican tweets.This approach has been evaluated relying on a collection of tweets released by the organizers of the shared task about aggressiveness detection in the context of the Ibereval 2018 evaluation campaign.The use of a benchmark corpus allows to compare the results with those obtained by Ibereval 2018 participant systems.However, looking at the achieved results, linguistic features seem not to help the deep learning classification for this task.
Exploiting Text Matching Techniques for Knowledge-Grounded Conversation

Knowledge-grounded conversation models aim at generating informative responses for the given dialogue context, based on external knowledge.To generate an informative and context-coherent response, it is important to conjugate dialogue context and external knowledge in a balanced manner.However, existing studies have paid less attention to finding appropriate knowledge sentences from external knowledge sources than to generating proper sentences with correct dialogue acts.In this paper, we propose two knowledge selection strategies: 1) Reduce-Match and 2) Match-Reduce and explore several neural knowledge-grounded conversation models based on each strategy.Models based on Reduce-Match strategy first distill the whole dialogue context into a single vector with salient features preserved and then compare this context vector with the representation of knowledge sentences to predict a relevant knowledge sentence.Models based on Match-Reduce strategy first match every turn of the context with knowledge sentences to capture fine-grained interactions and aggregate them while minimizing information loss to predict the knowledge sentence.Experimental results show that conversation models using each of our knowledge selection strategies outperform the competitive baselines not only in terms of knowledge selection accuracy but also in response generation performance.Our best model based on Match-Reduce outperforms the baselines in the comparative studies with the Wizard of Wikipedia dataset.Also, our best model based on Reduce-Match outperforms them with the CMU Document Grounded Conversations dataset.
The Fast Integration of a Rotated Haar-Like Feature for Face Detection

This paper addresses the problem of detecting rotated human faces in images.We propose a new face detection approach based on a novel extension of rotated Haar-like features, and a method to calculate their values based on the Bresenham algorithm of drawing a segment.The extension of the basic set of features, proposed by Viola and Jones, is done by the generation of a set of rotated Haar-likefeatures, by any angle, in an image.For the feature's integration process, we calculate the value of a rotated feature using the integral image technique, proposed by viola and jones.The main objective of this article is to propose a method to calculate the integral image of a rotated feature; we introduce the concept of key points that forms the four segments of a rotated rectangle.Our method focuses on three essential tasks; the first is to determine the rule for drawing a segment (SDR), the second is to identify all the key points of the rectangle r, and the third is to calculate the integral image.The speed of this method depends on the size and angle of rotation of the rectangle.Experimental results obtained using images from the MIT-CMU dataset are promising in terms of detection rate and the false alarm rate in comparison with other competing algorithms.
A Real-time Attendance System Using Deep-learning Face Recognition

Attendance check plays an important role in classroom management.Checking attendance by calling names or passing around a sign-in sheet is time-consuming, and especially the latter is open to easy fraud.This paper presents the detailed implementation of a real-time attendance check system based on face recognition and its results.To recognize a student's face, the system must first take and save a picture of the student as a reference in a database.During the attendance check, the web camera takes face pictures for a student to be recognized, and then the computer automatically detects the face and identifies a student name who most likely matches the pictures, and finally an excel file will be updated for attendance record based on the face recognition results.In the system, a pre-trained Haar Cascade model is used to detect faces from web camera video.A FaceNet, which has been trained by minimizing the triplet loss, is used to generate a 128dimensional encoding for a face image.The similarity between the encodings of two face images determines whether the two face images coming from the same students.The system has been used for a class, and the results are very satisfactory.A survey has been conducted to investigate the pros and cons of the attendance system on college education management.
On Human Faces Thermal Image Processing for Classification Purposes

This paper presents some results from a preliminary work on human faces thermal image processing for classification purposes. The main task is to find right representations and descriptors/features for high recognition rates. A data base with thermal images is created using people in various states, which reveals happiness, normal and sadness states. A toolbox based on Matlab modeling and simulation environment was designed and developed. The toolbox has specialized functions for image conversion from color to gray levels, format image conversion, image segmentation, objects detection and localization, face contours, image compression and analysis. Various data transform for image processing are considered, accompanied by information-based transforms, e.g. Renyi transforms. Classification is considered and it is based on similarity of the patterns. The results are very encouraging and show the flexibility of the toolbox and the potential of the proposed methods based on Discrete Cosines Transform (DCT) for feature selection and extraction.
Fuzzy Cognitive Map Based PI Controller Design

Fuzzy cognitive maps (FCMs) are often used in applications such as politics, health, engineering and information technologies. In control applications, FCMs are generally used as supervisor controllers. In literature, there are very few studies using the fuzzy cognitive map approach for the main controller design. In this study, three different PI-type controller models based on FCM approach are proposed. These models have 5, 4 and 3 tuning parameters, respectively. In order to show the effectiveness of the proposed FCM based controllers, simulation studies are performed on a nonlinear system. Performances of the proposed three models are compared with performances of a classical PI controller and a fuzzy PI controller with a 3 × 3 rule base. The comparison results show that the proposed three controller models exhibit better control performances than the classical PI and the fuzzy PI controller. The proposed fuzzy cognitive map based controller models can effectively be used as a main controller especially in nonlinear system control applications.