Reviewerly/exHarmony huggingface dataset:
DANet: Dynamic Salient Object Detection Networks Leveraging Auxiliary Information

Based on Fully Convolutional Networks, recent salient object detection (SOD) methods achieve impressive results. Some studies improve the classical SOD frameworks by utilizing auxiliary information like fixation points, salient numbers and salient edges. These works adopt auxiliary information by embedding sub-networks into the main network. However, how to incorporate auxiliary information regardless of the specific structure with less coupling is unexplored in SOD. In this paper, we present DANet, a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic</i> network to leverage arbitrary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Auxiliary</i> information for SOD. The proposed framework consists of 1) a Dynamic Weight Generator (DWG) which converts arbitrary auxiliary features into dynamic weights, 2) a Dynamic Bridge Block (DBB) which uses dynamic weight convolution to incorporate auxiliary information from DWG and then refines the fused features, and 3) a two-step training strategy to alleviate the side effect caused by drastic changes among different input images. Extensive experiments demonstrate the effect of different auxiliary information and the proposed framework is a universal method to improve SOD with auxiliary information. Comparison experiments show that DANet achieves state-of-the-art performance without any pre-processing and post-processing.
Multi-scale super-resolution reconstruction of a single image

Deep learning has been widely used in super-resolution reconstruction tasks in recent years. Most of the work is based on external examples, these methods have made great progress by training mapping functions from low-resolution (LR) image patches to high-resolution (HR) image patches compared with traditional methods. There are also a few methods focus on a single image and use the internal examples to get high-resolution images. The method based on prior knowledge obtains a large number of nonlinear mapping functions through complex convolution kernels, and significantly improves the reconstruction performance of the super-resolution task. However, these external example- based methods require a large number of patch pairs to train network parameters. Besides, most of the LR images are down-sampled from the ground truth images, not all the LR images in the real world come from the HR images, these images may be disturbed by noise, blurring and other factors, some LR images do not even have a corresponding ground truth image. These shortcomings make the training of the methods based on prior knowledge very time-consuming, and the reconstruction performance of specific images uncertain. Zero-short SR(ZSSR) firstly combines deep learning and internal examples together, and get satisfactory HR images at the test time. However, compared with the methods based on prior knowledge, ZSSR only uses the single image itself as the training dataset, directly learning the mapping functions between LR image patches and HR image patches does not fully display the self-similarity within the single image. In this paper, we further combine the internal mapping with deep-learning, learning internal mapping from different scales to get HR images with more fine details.
Blurring boundaries: Toward the collective empathic understanding of product requirements

Many software product companies create cross-functional development teams that own a product or a defined set of features. These product teams often require a deep and collective understanding of the product domain, a rich context within which to understand the product requirements and to make decisions throughout the development process. Little is known about what supports or impedes these teams in collectively achieving this deep understanding. This paper identifies certain organisational conditions that impact teams in this respect. Using Constructivist Grounded Theory method, we studied 18 teams across seven software companies creating products for a diverse range of markets. The study found certain organisational and planning process factors play a significant role in whether product development teams have the potential to collectively develop deep domain understanding. These factors also impact individual and development team dynamics. We identify two essential metaphorical dynamics, broadening the lens and blurring boundaries, that cross-functional product teams employ in order to fully embrace product ownership, visioning, and planning towards achieving this rich context for understanding product requirements. We also conclude that the highly specialised nature of many organisational models and development processes is contraindicated for cross-functional product development teams in achieving this deep collective understanding and we call for a revisiting of conventional organisational and product planning practices for software product development.
Open Source LaserGRBL – Arduino Based Laser Engraver

Nowadays technology is increasing rapidly, the usage and the implementation of CNC systems in industries and educational institutions are exponentially increasing but at a greater cost. Our main goal is to design a CNC Laser Engraver that is also a compact, cheap and low power unit that is also easy to operate in order to reduce complexity, cost and manual work. The idea of open source laserGRBL – arduino based laser engraver is to use the Open Source LaserGRBL that loads the G-code coordinates of an image that is given by user and sends those coordinates to the arduino which in turn signals the hardware components i.e. stepper-motor and laser to engrave onto the surfaces for example wood, Acrylic or plastic. The engraver will be able to engrave vector graphics in two (X & Y) axes of motion. Most of the engravers are designed in bi-directional movement and square type models. Whereas in this laser engraver, axes movements are mounted on each other and belt driven mechanism is used for engraving. Laser engraver uses a very fine matrix of dots to form images, such that a line may appear continuous to the naked eye, it in fact is a discrete set of points.
IDPchain: Blockchain-Based International Driving Permit and Traffic Crime Reporting System

A driving License is a card that defines whether the cardholder is eligible for the driving vehicles. An International Driving Permit (IDP) sanctions the holder to drive motor vehicles in peregrine countries. Generally, the driving license is credit card-sized plastic and the IDP is A6 sized paper document. Losing these documents especially in peregrine countries gets the holder into immensely colossal trouble. As it is not tough to make an unauthentically false IDP document, it raises the question of the validation of the IDP document. The IDP is issued by national agencies so that the peregrine country’s authority has to face arduousness to verify the IDP. Inadvertently, any driver may occur traffic violence in a peregrine country and get a penalty from the local authority. But there is no fruitful way to report the crime and charge the penalty against the driver. To tackle these issues, we propose a blockchain-based system where a peregrinator can have his IDP on the phone. Peregrine country’s local authority can check the validation of the IDP online. If the driver gets any penalty for his traffic breach, the local authority can easily report against him by utilizing our system. It keeps a 100% authentic track of each data transaction. All the data are stored in the blockchain that provides privacy, confidentiality, integrity, and reliability in our system. So, there is no chance of tampering any data. Our system ensures the user’s reliance on IDP.
İki Boyutlu Video Oyunlarında Sinir Stili Aktarımı Kullanarak Otomatik Oyun Mekaniği ve Estetiği Üretimi

Video game research is an ever-changing and dynamic area where sophisticated methods and algorithms are being developed. Procedural content generation (PCG), which aims to merge user-generated assets with algorithms to automate and improve video game content, has been the core of this sophistication. However, the outcomes are primarily reflected in game aesthetics, not in the game mechanics and gameplay. In this study, we introduce the “game scene as a canvas” concept where simple prototype game development pipelines, that can convert a 2D game-level image into a game development environment with ready-to-use colliders and artistically different styles that enhance the game aesthetics, are introduced. To do so, edge-based and color-based features of the input game level image are extracted using the Canny edge detector, Simple Linear Iterative Clustering, and Felzenszwalb segmentation. The Unity game engine is then used to generate colliders based on the provided edge and color features where the game level is style transferred with spatial control. Results of different neural style transfer algorithms are presented on benchmark games such as Super Mario and Kid Icarus. Results show that this study can become a promising tool to simplify 2D video game development, focusing on game mechanics and aesthetics.
Spark-Based FP-Growth Algorithm for Generating Association Rules from Big Data

Data mining is used to discover Business Intelligence Rules from large transactional database, frequent itemset mining and association rule mining are data mining techniques which are utilized for real-world applications using apriori algorithm as well as FP-Growth algorithm. To extract frequent itemset and association rules from the grocery store using traditional apriori algorithm takes time because it generates candidate key for each item in the dataset, whereas in FP-Tree without generating any candidate key it finds frequent items and association rules for the grocery dataset by constructing FP-Tree, and it is time consuming. To overcome this problem, we propose a method called MapReduce-based FP-Tree algorithm which generates frequent patterns and association rules by using parallel computations to reduce computational time. The experimental results show that time taken for generating frequent patterns and association rules for the grocery dataset is less.
Human Activity Classification Using Machine Learning Techniques with Feature Selection

Recognition of human activities is a field of rising interest in the present transformation. Recognizing the activities allows the applications in constructing the activity profiles for every subject that could be used efficiently for the health and safety applications. Nowadays smartphones are upgraded with remarkable motion sensors to revitalize the entryways in machine learning. According to human activity recognition, datasets for the corresponding work have been increased that paves the way for certainties in different research areas. The human activity recognition dataset includes accelerometer sensor data with respect to six different human activities for example sitting, standing, lying, walk, upstairs walk, downstairs walk. The paper presents the human activity recognition process by analyzing the filter-based feature selection methods in the machine learning model. For activity classification, various classifiers are used as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Random Forest and Decision Tree classifiers. The performance of the model is validated using Train/test split approach. Therefore, for activity recognition, we proposed a model giving better results with a random forest classifier and correlation-based feature selection with best-first search.
Penerapan RAD pada Aplikasi E-Learning Lembaga Bimbingan Belajar Gold Generation

The development of information and technology has helped people to do their activity. In future living, information and technology will be the most dominant sector. Technology takes part in most of the aspects such education. Technology’s role is urgently needed, the example is Gold Generation Tutoring Agency as their service quality improvement. During this time tryout and tasks is done in a paper and not recorded on a database. It complicates the agency officers to manage the students’ grade and their tasks. And tutors find themselves in difficult situation in monitoring their students. Another popular name at the moment is E-Learning, it is one of learning process models that utilize media of information and technology communication especially internet. By the existence of e-learning that is directly connected to a database will ease the admin and the officers of the tutoring agency to manage the students grade and correct their task results, and not only for better service, but also for easing the students with lesson materials. According to the things mentioned above, "Web-based E-Learning Application of Gold Generation Tutoring agency" is made. This application is expected to help in managing students grade and correcting their tasks in Gold Generation Agency
Research on Influencing Factors and Optimization of College Students’ Learning Behavior Based on Mobile Learning

With the rapid development of the Internet, mobile learning environment is rapidly and profoundly affecting the learning behavior of college students in the new era. This paper investigates the current situation and problems of college students' learning behaviors under the mobile learning environment by using a combination of questionnaire survey and interview with undergraduates in a certain university in a certain region as the research object. The survey results show that from the learning behavior of mobile learning environment, it can be seen that college students have a great demand for online learning, but most of them have not found a reasonable way of learning, resulting in low learning efficiency. In view of the existing problems, the author proposes corresponding online learning strategies, so that college students can exert greater learning potential in the mobile learning environment.
Formation of the New Multi-sender Authentication Codes Via Symplectic Space through Finite Commutative Rings with Applications

The multi-sender authentication codes allow the group of senders to construct the authenticated message. A receiver can verify the authenticity of the received message. We develop multi-sender authentication codes to use a symplectic geometry over finite commutative rings. The parameters and the overall chance of deception are also evaluated. So we reduce the probability of success of the impersonation and substitution attack.
Crafting Sound: Simple Sonic Interfaces for Education and Creation

This course will prepare participants to incorporate sonic arts and crafts in their pedagogical practice or educational research in remote and in-person contexts. These skill sets complement a traditional electronics education toolkit through the authentic integration of visual and sonic arts. The course will be taught in three sections. We provide an introduction and overview for the course (15 minutes), introduce listening games as tools for critical inquiry and integrated learning (30 minutes), present graphic musical scores and sonic interfaces for STEAM learning (45-minutes), and share resources for kit-based STEAM circuits for remote learning (60 minutes).
An Analysis on the Feasibility of a Low-cost Fall Detection System

There are various fall detection algorithms that have been developed as the solution to the increase on the risk of fall on elderly. A fall detection system which is low-cost is certainly favored, but that pose another question on whether the system is accurate enough by using low-cost components. This paper will discuss the analysis on the feasibility of a threshold-based fall detection system that was realized with ADXL 345 sensor, which is considered as a low-cost accelerometer sensor. A simulated fall study and simulated Activities of Daily Living (ADL) study was conducted so that the accuracy of the realized system can be known. The result of this experimental study shows that the realized system has a satisfactory accuracy comparable to other systems which use more advanced sensor(s), but further study which involves elderly as subjects is needed to give a more accurate insight about the accuracy of the system.
SocialSciTerm: An English-Chinese Parallel Term Resource for Collaborative Translation

Bilingual term resources are helpful on collaborative translation tasks. Firstly, we build an English-Chinese parallel term resource of social sciences (SocialSciTerm) based on the bilingual big data and advanced theories of information extraction and alignment. Secondly, we verify the SocialSciTerm’s accuracy and practicality through a translation task of social sciences. The statistical results indicate that the coverage rate is high in common entity names and terms of social sciences disciplines while that of text-specific word still needs to be improved. Finally, some advice is proposed for the future study of information extraction and alignment.
Capacity Theorems for Covert Bosonic Channels

We study quantum-secure covert-communication over lossy thermal-noise bosonic channels, the quantum mechanical model for many practical channels. We derive the expressions for the covert capacity of these channels: L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no</sub> -EA, when Alice and Bob share only a classical secret, and LEA, when they benefit from entanglement assistance. Entanglement assistance alters the fundamental scaling law for covert communication. Instead of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no</sub> -EA√n-r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no</sub> -EA(n), r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no</sub> -EA(n) = o(√n), entanglement assistance allows L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EA</sub> √n log n - r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EA</sub> (n), rEA(n) = o(√n log n), covert bits to be transmitted reliably over n channel uses. However, noise in entanglement storage erases the log n gain from our achievability; work on the matching converse is ongoing.
Planning and acting in dynamic environments: identifying and avoiding dangerous situations

In dynamic environments, external events might occur and modify the environment without consent of intelligent agents. Plans of the agents might hence be disrupted and, worse, the agents might end up in dead-end states and no longer be able to achieve their goals. Hence, the agents should monitor the environment during plan execution and if they encounter a dangerous situation they should (reactively) act to escape from it.In this paper, we introduce the notion of dangerous states that the agent might encounter during its plan execution in dynamic environments. We present a method for computing lower bound of dangerousness of a state after applying a sequence of actions. That method is leveraged in identifying situations in which the agent has to start acting to avoid danger. We present two types of such behaviour – purely reactive and proactive (eliminating the source of danger). The introduced concepts for planning with dangerous states are implemented and tested in two scenarios – a simple RPG-like game, called Dark Dungeon, and a platform game inspired by the Perestroika video game. The results show that reasoning with dangerous states achieves better success rate (reaching the goals) than naive planning or rule-based techniques.
Research and Practice of Virtual Simulation Experiment System for Planning and Control of Digital Factory

More and more attention has been paid to the comprehensive quality and the innovative ability to solve complex engineering problems in cultivating talents in universities. Production Planning and Control is one of the core courses of Industrial Engineering specialty, its syllabus clearly requires students to master the whole process of production planning and control of enterprises, but enterprises cannot allow students to participate in the real production process, and colleges and universities cannot build factories for students to do experiments. The bottleneck of poor teaching effect has been unable to break through. Nowadays, relying on the accumulation of scientific research projects, using virtual simulation technology to build virtual simulation experimental system has become the best choice to provide students with unlimited and repeatable "experimental sites" that can be participated in at any time and eliminate the "blind spots" of traditional teaching methods. Based on the real production data of enterprises, the School of Mechanical and Electrical Engineering of Wuhan University of Technology has made great efforts to build a "Virtual Simulation Experimental System for Planning and Control of Digital Factories", which has been applied to teaching and achieved good experimental results.
A Hierarchical Attention-Based Neural Network Model for Socialbot Detection in OSN

In this paper, we present a hierarchical attention-based deep neural network model for socialbots detection in online social networks through modeling user behavior at two levels of granularity leveraging profile, activity, temporal, and content information. The proposed approach is novel from two perspectives - (i) it models user representations using a comprehensive set of profile and behavior information, and (ii) it applies hierarchical attention mechanism at both low-level and high-level user representations. The proposed approach jointly models profile, temporal, and activity information as sequences, which are given as input to a two-layer stacked BiLSTM, while content information is given to a six-layer CNN. On evaluation, our model performs significantly better in comparison to the baselines and state-of-the-art methods.
Exploring Understanding and Usage of Two-Factor Authentication Account Recovery

Users employ strategies that make passwords weaker than they appear. Companies have started requiring users to adopt two-factor authentication (2FA) to increase security, making regaining account access difficult. A potential solution is to provide users with an account recovery method used as a failsafe for 2FA in the event of a lost, broken, or stolen second factor. However, limited research evaluates users’ employment of different types of 2FA account recovery methods. Our exploratory study surveyed 103 students. We found only 40% of the sample have more than one 2FA device enrolled per personal account. The majority opt to use 2FA devices that are executed using their phone, 81% use a mobile app, and 51% receive an SMS text message. Our findings suggest regaining account access could be challenging for users when their phone becomes unavailable. If companies do not adequately prepare for such occurrences, it will become costly and disruptive.
Bi-Directional Selectivity Refinement Network for Salient Object Detection

Salient object detection is a fundamental computer vision task. The majority of current algorithms focus on the use of edge information. However, these algorithms are limited to one-way auxiliary training or feature fusion to improve salient features. In this paper, we propose a novel framework for salient object detection, called Bi-Directional Selectivity Refinement Network (BSRN). Our framework aims to simultaneously refine salient features and perfect edge features through a Bi-directional Selectivity Refinement Module (BSRM). In this process, we innovatively combine short connections and attention mechainsm to fully use multi-scale features, selectively enhance features, reduce redundancy and suppress distractors. Besides, in order to improve the adaptability to detect multi-scale objects and achieve better result, we also propose a Multi-scale Feature Extraction Module (MFEM) to capture global contextual information. Extensive experiments conducted on four benchmark datasets demonstrate that our method outperforms 10 similar methods.