Reviewerly/exHarmony huggingface dataset:
Sequence Learning with Side Dependencies

Numerous sequence learning models have been proposed to capture the sequential dependencies and have achieved remarkable success in a wide range of applications. In addition to sequential dependencies, real sequential data also exhibits side dependencies. For example, in session-based recommender systems, items are naturally related because of their intrinsic attributes such as brand, category, and function; and in document modeling, words are inherently related since they can share similar syntactic functions. Intuitively, such side dependencies provide rich information beyond the sequence (or sequential dependencies); thus they have great potential to advance traditional sequence modeling. However, research on exploring side dependencies for sequence learning is rather limited. In this paper, we study the problem of sequence learning with side dependencies. In particular, we propose a novel sequence learning framework SEE, which can simultaneously capture both sequential and side dependencies. Extensive experiments on real sequential data demonstrate the effectiveness of the proposed framework SEE and the advantages of integrating side dependencies.
A new scalable semantic Web system based on Big Data: A use case in the mobile learning

The semantic web allows machines to understand the meaning of data and to make better use of it.. Resource Description Framework (RDF) is the liagna franca of Semantic Web. While Big Data handles the problematic of storing and processing massive data, it still does not provide a support for RDF data. In this paper, we present a new Big Data semantic web comprised of a classical Big Data system with a semantic layer. As a proof of concept of our approach, we use Mobile-learning as a case study. The architecture we propose is composed of two main parts: a knowledge server and an adaptation model. The knowledge server allows trainers and business experts to represent their expertise using business rules and ontology to ensure heterogeneous knowledge. Then, in a mobility environment, the knowledge server makes it possible to take into account the constraints of the environment and the user constraints thanks to the RDF exchange format. The adaptation model based on RDF graphs corresponds to combinatorial optimization algorithms, whose objective is to propose to the learner a relevant combination of Learning Object based on its contextual constraints. Our solution guarantees scalability, and high data availability through the use of the principle of replication. The results obtained in the system evaluation experiments, on a large number of servers show the efficiency, scalability, and robustness of our system if the amount of data processed is very large.
Asynchronous Stochastic Gradient Descent over Decentralized Datasets

Asynchronous stochastic gradient descent (ASGD) usually works in the centralized setting in which workers retrieve data from a shared training set. This paper focuses on decentralized scenarios where each worker only accesses a subset of the whole training set. We find that due to the heterogeneous properties of the decentralized setting, ASGD will optimize in wrong directions and thus obtain poor solutions. To tackle the issue, a novel algorithm DASGD is proposed for above setting. Our key idea is to form an asymptotically unbiased accurate gradient estimate through reweighting stochastic gradient based on importance sampling technique. Numerical results substantiate the performance of the proposed algorithm in the decentralized setting.
A Short Survey: Behavioral Authentication Using Mobile Sensors

Nowadays, mobile devices are used in communication to send and receive sensitive data. To protect user’s personal and sensitive data, PIN, pattern, and passwords are the most commonly used techniques, but these techniques have several drawbacks such as password or PIN can be stolen or forgotten due to memory loss. In this regard, biometric approaches provide more security than these traditional authentication approaches (PIN/password). Biometric techniques are divided into physiological and behavioral techniques. Certain physiological techniques are being used nowadays like fingerprint and iris scanner, but these techniques need special hardware that is not present in every device, also these techniques can also be compromised. This paper presents a short survey of different behavioral authentication methods based on the basic sensors that are present in all existing smartphones. Moreover, in this paper, different challenges and open issues for future directions are also discussed to obtain higher accuracy, reliability, and universality.
API analytics for curating static analysis rules

Use of third-party library APIs is pervasive, but can be error-prone. API-usage errors can be detected via static analysis if specifications of correct usage are available, but manually creating such specifications is a bottleneck. We showcase a semi-automated "big code" solution, where we use large code corpora to mine patterns in API usage, and ask human experts to perform analytics on those patterns to create static analysis rules.
Optimization communication for BFS based on 1D-partition

Abstract Parallel Breadth First Search (BFS) is a famous algorithm in Graph500, a benchmark function for evaluating data-intensive applications on supercomputers. For parallel breadth first search (BFS) algorithms on large-scale distributed memory systems, the cost of communication is usually much higher than the arithmetic cost, which limits the scalability of the algorithm. However, the specific communication model of Graph500 brings challenges to computing in large-scale graphs. First, we use an adjustment method to delete redundant data in messages. Second, a data compression method was used to further reduce comm-unication. Evaluation results show that the performance of this method is more efficient than graph500 benchmark.
An Intelligent Maintenance and Repair Informing System Using Machine Learning Techniques for Solar Power Generation Equipment

This paper proposes an intelligent maintenance and repair informing system for solar power generation equipment, which the machine-learning techniques adopted to improve the time for manually judging the abnormal operation of the equipment. Thereby the maintenance efficiency of the equipment can be enhanced to reduce solar power generation losses. Based on the current environmental sensing data and power generation data of equipment, the proposed system infers whether the status of the solar power generation of each equipment is abnormal and its possible causes. Moreover, the maintenance and repair informing procedures can be processed and decided within 15 minutes.
Evolutionary Box Particle Filter for Nonlinear Estimation

This paper presents a new box particle filter that makes use of evolutionary strategy to assist box contraction. Box contraction is frequently referred to as a constraint satisfaction problem. Since box particles exist in the interval space, this brings an issue called the wrapping effect. To solve this issue, the evolutionary algorithm is taken. Even though evolutionary algorithm is an eminent framework, the evolutionary algorithm itself cannot effectively solve this box contraction issue since genetic operators are blind to constraints. For this reason, the proposed method adopts a repairing process to manage constraints. The proposed evolutionary strategy checks the consistency of box particles based on population fitness and individual fitness. Individual fitness finds defective chromosomes, and population fitness works as a slack variable for each box particle. Both finesses are updated for each contraction which makes evolutionary contraction works as a reinforcement learning. A simple computer simulation shows the efficiency of the proposed evolutionary box particle filter.
Web Development Using WISDM and RAD

The web development project for the business organization must be covered not only stakeholder needs but also completed in limited time. This research attempted to present how to use WISDM and RAD methodology as well as supporting by SUS method and rich picture for completing the time-intensive project and meeting stakeholder needs. A case study was undertaken by accomplishing web development project named SysIO and Sysbatik. As a result, the methodology phase that is consisted of requirement planning (rick picture, organizational analysis, information analysis), user design (HCI), construction (technical design, SUS method), cut over (work design) can be a guide to complete both of projects.
Free Text Analysis in Electronic Clinical Documentation

This study finds reason in the now wide availability of clinical documentation stored in electronic form to track the patient’s health status during his care path. The diffusion of these practices make available many biomedical collections of electronic data, easily accessible at low cost that can be used for research purposes in the field of observational epidemiological studies, in analogy with what was historically already done in studies based on the reviewing of medical records. However, since these collections are not organized according to specific survey schemes, they sometimes do not allow the index events to be discriminated with the necessary reliability between one source and another automatically. This poses the problem of finding effective methods of critical rereading of texts able of solving this deficiency and, according to the possibility, of bringing the words or segments back into subdomains that can be analyzed statistically. It is proposed to address the problem, showing study criteria and an empirical experience, consistent with the needs of a biomedical context.
Design and implementation of intelligent experiment management platform based on Internet of things

In order to solve the current situation of low management efficiency in university laboratories and the inconvenience to use it, this paper proposes and designs an intelligent experiment management system based on Internet of things technology. This system puts forward a feasible and detailed design scheme, which adopts the Internet of things technology to design the intelligent experiment management system and uses Java to realize the intelligent experiment management platform. Finally, the function of the management system is tested and verified. The test shows that the intelligent experiment management system has basically achieved the expected functions and meets the needs of the intelligent experiment management platform of universities.
Crank Call Detection Models Based on Call Data of Telephone Subscribers

Abstract Data explosion makes our life much more convenient than ever. For example, people always get timely news from Apps, and get interesting recommendations when shopping online. However, these conveniences come by probing people’s private data, such as account information, search history, and chat records. Thus, if the management of data is not as secure as it supposes to be, many problems, e.g., crank calls, will arise and cause big troubles in people’s lives. Although many security Apps have been developed to help mobile phone users stay away from crank calls, the users need to update Apps frequently due to their user-labels-based blacklist. These feedback-based systems depend on a large number of user actions that have significant lag. In this paper, we propose two crank call detection models to help operators mark crank callers based on the call data of telephone subscribers, in which the features are organized by different ways. One is a composited classification model, and the other is a convolutional neural network model based on the time-sliced data. The experiments show that the model applying convolutional neural network gets better accuracies in detecting crank calls.
Calculation the Determinants of matrix by Permutation Algorithm by fixing two components by Computer

Abstract In this paper, we will present a complementary algorithm for calculating the determinants order of n × n, ((n ≥ 5) Matrixes. This algorithm is based on the proposed ideal algorithm for permitting the fixing of two components. Programming this algorithm in Matlab as an application on permutations algorithms to finding determinants.
Collocated Sharing of Presentations of Self in Public Settings

Figure 1: The six field sites: (a) a meetup of travellers in a bar, (b) a shopping centre, (c) a university library, (d) a bus station, (e) a museum and (f) a technology meet up in an office space.See section 3.1 or table 1 for details
The Space of an Image Existence. What do the Internet the Ancient City and the Medieval Temple Have in Common?

Introduction. The article discusses various historical examples of symbolic spaces – spaces of the existence of an image – to consider how representation systems are arranged in them and how they set the tone for the complex process of creating an image of a person. The author does not consider the image as a conscious construct, which is completely dependent on this external system, but supposes that it is important to research it for a deeper understanding of the modern version of symbolic space and all complex and interrelated processes in it. Thus, the article provides theoretical grounds for a legitimate comparison of practices of the Internet and social media representation with the practices of the past which unfolded in specific, semiotic toposes: Egyptian murals in tombs, the ancient city, a medieval temple, and a European text of the Modernity. Methodology and sources. The theoretical basis of this research is on the contact of a number of approaches to the analysis of the image and image systems: representationst, phenomenological, philosophical-anthropological, (post)structuralist. The author uses works in historical, cultural and art studies in the respective eras as soursces of information on individual spaces of representation of the past (B. Manley, M. Bird, D. Yu. Dorofeev, V. Svetlov, S. Zotov, M. Maizuls, M. Foucault and others). The author relies on detailed descriptions of public spaces, identifies and compares their characteristic features. Results and discussion. Having examined the main features of a number of symbolic spaces of the past, the author shows how they inevitably affect the process of building an image, creating the system of representation. In this sense, both “city” and “text” are presented in the article as concepts, semantic fields and structures, and not as physical objects. The author comes to the conclusion that there is no so much novelty of the modern processes of symbolic exchange on the Internet, as it is usually declared. Most of the actual space`s features which today are called new are found in other spaces and other times. Polyphony, the visual component, one-to-many and many-to-many message addressing, active use of “ready-made” markers and symbolic “blanks”, the iconic character of the signs used – these features of modern communication in the Internet space seem to be new only in comparison with communication within the framework of the New European text. These features are quite applicable to the ancient city and to the medieval temple. Conclusion. The author shows a strong similarity of the modern Internet precisely with an ancient city and a medieval temple, while the text of the Modernity differs from them, and that creates the “novelty” of the current situation. This does not diminish interest in modern practices of representation since their specificity does not necessarily have to be built through the position of innovation which was valuable in the culture of the Modernity.
Luminance Ratios for Stable SIFT Keypoint Detection in Challenging Illuminations Scenarios

Invariance to changes in scene illumination is a desirable feature in computer vision, but its accomplishment is still a challenge even for modern systems. This paper proposes a novel way to detect keypoints in scale-space by exploring the concept of luminance ratio first introduced in Wallach's Lightness Constancy Theory, instead of using linear difference operators. The hypothesis is that an operator based on the concept of luminance ratio may provide more robust results with respect to illumination changes. In order to test this hypothesis, the SIFT keypoint detector was adapted to the proposed approach with minimum implementation effort. Experimental results show that the luminance ratio approach indeed yields more stable keypoint detection than the original SIFT based on linear difference operators, specially in lower illumination conditions. Existing SIFT implementations can be easily adapted to use the luminance ratio concept for improved operation in environments with uncontrolled lighting conditions.
Augmented Reality Technology in Education

Abstract Augmented reality technology has entered the field of education, and augmented reality is one of the innovations of technology. The educational institutions that allow uniqueness and enrichment of educational jobs through feeding them with many different sources and alternatives, which constitute an integrated combined methodological unit aimed at achieving ideal learning that is characterized by a large degree of effectiveness, efficiency and mastery and supports the learning process through its new and different by adding audio, 3D, and video. This technology transmits information by adding a new virtual image (audio visual information) to the still image (the real world) and this allows the learner to interact with this technology by dealing with additional information or objects that are displayed in the real world. In this paper, the proposed method for promoting and supporting the traditional method of teaching and converting difficult and complex materials into easy materials is presented easy to understand and memorize, which is called Technology Enhanced Learning (TEL). In this technique is produced virtual elements such as explanatory video, audio and three-dimensional shapes, these elements are stored in the database and linked to the note in the study material created when the learner focus the phone camera towards the mark, then camera picks up the mark, and opens the default elements on the phone screen.
A Study of the Agile Coach’s Role

While Agile Software Development has recently received much attention in industry, the implementation of agile methods and practices is not straightforward, partly because the approach focuses on people and their interactions. To facilitate the adoption of agile methods and the further development of agile teams, the role of an agile coach has been introduced. However, this role has not been sufficiently explored from the perspective of those whose perform that role. To address this gap, we conducted a case study where we interviewed agile coaches with experiences from multiple companies. We identified the main objectives of agile coaches, the methods they use to achieve those objectives, the challenges they frequently face, the skills required by the role, and the value they provide to organizations and teams. Our findings contribute to a better understanding of the role and the construction of a professional identity. In addition, we offer a characterization that can be useful for professionals that are new in the role.
VoteXX: A Remote Voting System that is Coercion Resistant

Sherman was supported in part by the National Science Foundation under SFS grant DGE-1753681, and by the U.S. Department of Defense under CySP grant H98230-19-1-0308.
A Low-Complexity and Hardware-Oriented Image Stitching Algorithm

This paper presents a hardware-oriented image stitching algorithm with characteristic of low complexity. The proposed algorithm is developed by using the feature from accelerated segment test (FAST) algorithm to extract feature point, and combines scale invariant feature transform (SIFT) descriptor to perform feature matching. In order to be suitable for hardware implementation, the proposed algorithm was simplified by using the optimal seam algorithm widely used to achieve seamless image stitching and eliminate ghosting. Moreover, the obvious lines at the stitching seam due to the color difference are reduced by the low-pass filter. Experiment results show that the proposed algorithm has benefits of seamless images with high quality and calculation with low complexity.
LAC-Nav: Collision-Free Multiagent Navigation Based on the Local Action Cells

Collision avoidance is one of the most primary problems in the decentralized multiagent navigation: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduced the concept of the local action cell, which provides for each agent a set of velocities that are safe to perform. Consequently, as long as the local action cells are updated on time and each agent selects its motion within the corresponding cell, there should be no collision caused. Furthermore, we coupled the local action cell with an adaptive learning framework, in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.
Proposal of Pseudo-Random Number Generators Using PingPong256 and Chaos Maps

Internet of Things (IoT) devices are easily exposed to physical attacks; therefore, their design must consider authentication and encryption. Many authentication and encryption methods use algorithms such as advanced encryption standard (AES) and secure hash algorithm (SHA). A pseudo-random number generator (PRNG) can also be used for authentication and encryption, and linear feedback shift register (LFSR) provides an easy way to generate PRNGs. LFSR allows the mathematical generation of unique values ​​proportional to a given length. However, as LFSR is mathematically predictable, it is not used alone for this purpose. PingPong256 uses a variable clock for LFSR that can generate very long periods. However, LFSRs are still potentially at risk of being attacked by correlation analysis attacks. There are several methods to account these security issues, including chaos maps (such as logistic maps), SHA, and AES. This paper proposes a method of using logistic maps corresponding to PingPong256 and chaos maps. For this purpose, various PingPong256 configurations are proposed and compared to verify the effectiveness of the proposed method. The method was tested using NIST SP800-22.
3D Object Detection from Consecutive Monocular Images

Detecting objects in 3D space plays an important role in scene understanding for real applications, such as autonomous driving and mobile robot navigation. Many image-based methods have been proposed due to the high cost of LiDAR. However, monocular images are lack of depth information and difficult to detect objects with occlusion. In this paper, we propose to integrate 2D/3D object detection and 3D motion estimation for consecutive monocular images to overcome these problems. Additionally, we estimate the relative motion of the object between frames to reconstruct the scene in the previous timestamp. To learn motion estimation from unlabeled data, we propose an unsupervised motion loss which learns 3D motion estimation from consecutive images. Our experiments on KITTI dataset show that the proposed method outperforms the state-of-the-art methods for 3D Pedestrian and Cyclist detection and achieves competitive results for 3D Car detection.
Biorthogonal wavelet-based multigrid method for the numerical solution of elliptic partial differential equations

Wavelets have generated significant interest from both theoretical and applied researchers over the last few decades. We proposed biorthogonal wavelet-based multigrid method for the numerical solution of elliptic partial differential equations. The performance of the proposed scheme is better than the existing ones in terms of superconvergence with low computational time. Some of the illustrative examples demonstrate the applicability of the method.