Miracle-dz/UniFAR-FTUs huggingface dataset:
Patch-based Segmentation of Latent Fingerprint Images Using Convolutional Neural Network

ABSTRACT Latent fingerprint segmentation involves marking out all the foreground regions accurately in a latent fingerprint image, but due to poor quality images and complex background, segmentation of latent fingerprint images is one of the most difficult tasks in automatic latent fingerprint recognition systems. In this article, we propose a patch-based technique for segmentation of latent fingerprint images, which uses Convolutional Neural Network (CNN) to classify patches. CNN has recently shown impressive performance in the field of pattern recognition, classification, and object detection, which inspired us to use CNN for this complex task. We trained the CNN model using SGD to classify image patches into fingerprint and non-fingerprint classes followed by proposed false patch removal technique, which uses “majority of neighbors” to remove the isolated and miss-classified patches. Finally, based on the final class of patches, an ROI is constructed to mark out the foreground from the background of latent fingerprint images. We tested our model on IIIT-D latent fingerprint database and the experimental results show improvements in the overall accuracy compared to existing methods.
Enabling Efficient Job Dispatching in Accelerator-Extended Heterogeneous Systems with Unified Address Space

In addition to GPUs that see increasingly widespread use for general-purpose computing, special-purpose accelerators are widely used for their high efficiency and low power consumption, attached to general-purpose CPUs, thus forming Heterogeneous System Architectures (HSAs). This paper presents a new communication model for heterogeneous computing, that utilizes a unified memory space for CPUs and accelerators and removes the requirement for virtual-to-physical address translation through an I/O Memory Management Unit (IOMMU), thus making stronger the adoption of Heterogeneous System Architectures in SoCs that do not include an IOMMU but still representing a large number in real products. By exploiting user-level queuing, workload dispatching to specialized hardware accelerators allows the removal of drawbacks present when copying objects through using the operating system calls. Additionally, dispatching is structured to enable fixed-size packet management that hardware specialized logic accelerates. To also eliminate IOMMU performance loss and IOMMU management complexity, we propose direct accelerator data placement in contiguous space in system-memory, where, the dispatcher provides trasparent access to the accelerators and at the same time offers an easy abstraction in the programming layer for the application. We demonstrate dispatching rates that exceed ten thousand jobs per second implementing architectural support on a low-cost embedded System-on-Chip, bounded only by the computing capacity of the hardware accelerators.
Programming Smart Contracts in Ethereum Blockchain using Solidity

This workshop introduces participants to programming smart contracts using Ethereum Blockchains and the Solidity programming language. Cryptocurrencies such as Bitcoins use Blockchains and Smart Contracts to enforce transactions. Given the popularity of Bitcoins and related technologies in the press, this module provides a module for CS educators to introduce the underlying technology into their classrooms. Participants receive handouts describing sample programming techniques and worksheets for creating basic smart contracts. The workshop proceeds in three sessions in which we: present the underlying technology of Ethereum; practice the creation of smart contracts using the Solidity programming language; and discuss the implementation of this module in our classrooms in small groups. Further information, sample code and workshop handouts are at: http://maui.hawaii. edu/cybersecurity. Note: A laptop is needed for this workshop and handouts will be given out.
A Case Study on Optimizing Accurate Half Precision Average

In this work, we study the numerical performance of various common algorithms used to calculate the average of an array of half precision (FP16) floating point values. While the current generation of CPUs does not support native FP16 arithmetic, it is a planned feature in a number of next-generation CPUs. FP16 arithmetic was emulated via the half software library. Due to the limitations of the FP16 data type, some algorithms proved insufficient for arrays as small as 100 elements. We propose an algorithm that allows numerically stable FP16 computation of the average and compare it to the naive floating point (FP32) algorithm in terms of both numerical precision and runtime performance. We find that our algorithm offers comparable robustness, numerical precision, and SIMD performance to the higher precision computation.
Natural Interaction in Augmented Reality Context

In recent years, immersive technologies like Virtual and Augmented Reality have been accelerating at an incredible pace, building innovative experiences and developing new interaction paradigms. Current research has widely explored gesture interaction with Augmented Reality interfaces, but usually requires users to manipulate input devices that could be cumbersome and obtrusive, thus preventing them from interacting efficiently with the 3D environment. Therefore, Natural User Interfaces and freehand gesture interaction are becoming more and more popular, improving the user’s engagement and sense of presence, providing more stimulating, user-friendly and non-obtrusive interaction methods. However, researchers argue about the impact of the interaction fidelity in usability and user satisfaction, questioning the level of naturalness that should characterize the interaction metaphors. Current paper proposes different gesture recognition techniques for three basic interaction categories (translation, rotation and scaling) in a Leap Motion Controller Augmented Reality framework. A prototype is implemented in order to evaluate efficiency and usability of the proposed architecture. Finally, experimental results are discussed.
Exploring Leonardo Da Vinci's Mona Lisa by Visual Computing: a Review

This paper surveys on relevant aspects of Leonardo Da Vinci’s Mona Lisa, one of the most important pieces of art worldwide, from a visual computing perspective. This is accomplished by describing stateof-the-art works in the areas of image analysis and human computer interaction advancing hypotheses about the identity, ambiguity and hidden features of Mona Lisa’s portrait. The second part of the paper is dedicated to describing computer graphics models in 2D and 3D for capturing the visual details of the portrait, in order to discover new features and advancing new hypotheses about the painting. Finally, the di↵erent works are discussed and a suggestion for future work is proposed. This paper can be particularly useful to computer vision, applied mathematics and statistics as well as art and history research communities, in order to understand the current literature methods, their limitations, and explore new directions for shedding light on a mystery still partially unsolved in
Accessible AST-Based Programming for Visually-Impaired Programmers

Most programmers rely on visual tools (block-based editors, auto-indentation, bracket matching, syntax highlighting, etc.), which are inaccessible to visually-impaired programmers. While prior language-specific, downloadable tools have demonstrated benefits for the visually-impaired, we lack language-independent, cloud-based tools, both of which are critically needed. We present a new toolkit for building fully-accessible, browser-based programming environments for multiple languages. Given a parser that meets certain specifications, this toolkit will generate a block editor familiar to sighted users that also communicates the structure of a program using spoken descriptions, and allows for navigation using standard (accessible) keyboard shortcuts. This paper presents the toolkit and a first evaluation of it. While the toolkit allows for full editing of code, we chose to focus strictly on navigation for this evaluation, using the navigation-only study design of Baker, Milne and Ladner. Visually-impaired programmers completed several tasks with and without our tool, and we compared their results and experience. Users had improved accuracy when completing tasks, were significantly better able to orient when reading code, and felt better about completing the tasks when using the tool. Moreover, these improvements came with no significant change in task completion time over plain text, even for experienced programmers who navigate text using screen readers set to high words-per-minutes.
Autocomplete vocal-fo annotation of songs using musical repetitions

Audio annotation for music clips is an important task for machine-learning-based music analysis and applications. However, it is a time-consuming task because it often requires repetitive manipulations even though typical audio files often contain repetitive structures (e.g., a song often has similar phrases used multiple times). In this paper we present a new interaction technique, to intelligently automate repetitive manipulations for audio annotation. It mimics the "autocompletion" functions used in source code editors and spreadsheet software and is called Autocomplete Audio Annotation. We developed a proof-of-concept system for annotating the continuous fundamental frequency (fo) of a vocal part of a song.
Multilevel Weighted Feature Fusion Using Convolutional Neural Networks for EEG Motor Imagery Classification

Deep learning methods, such as convolution neural networks (CNNs), have achieved remarkable success in computer vision tasks. Hence, an increasing trend in using deep learning for electroencephalograph (EEG) analysis is evident. Extracting relevant information from CNN features is one of the key reasons behind the success of the CNN-based deep learning models. Some CNN models use convolutional features from different CNN layers with good effect. However, extraction and fusion of multilevel convolutional features remain unexplored for EEG applications. Moreover, cognitive computing and artificial intelligence experience increasing applications in all fields. Cognitive process is based on understanding human brain cognition through signals, such as EEG. Hence, deep learning can aid in developing cognitive systems and related applications by improving EEG decoding. The classification and recognition of EEG have consistently been challenging due to its characteristics of dynamic time series data and low signal-to-noise ratio. However, the information hidden in different convolution layers can aid in improving feature discrimination capability. In this paper, we use the EEG motor imagery data to uncover the benefits of extracting and fusing multilevel convolutional features from different CNN layers, which are abstract representations of the input at various levels. Our proposed CNN model can learn robust spectral and temporal features from the raw EEG data. We demonstrate that such multilevel feature fusion outperforms the models that use features only from the last layer. Our results are better than the state of the art for EEG decoding and classification.
Early Programming Education and Career Orientation: The Effects of Gender, Self-Efficacy, Motivation and Stereotypes

Programming education currently begins at the elementary school age. In this paper we are exploring what affects the learning performance of young students in programming classes. We present the results collected during an eight-week experimental Scratch programming course run in elementary schools. We emphasize factors that have been found to affect learning performance in adult students, including self-efficacy and motivation, and measure how they affect students of this age group. We further explore the students' view of programming as a career path, and measure the effects of the course, their performance, and the stereotypes that they assume for computer scientists. We find that students' intrinsic and extrinsic motivation and previous programming experience are important factors, being strongly correlated with their self-efficacy and their inclination towards a CS career. For female students only, we also find CS career orientation to be strongly correlated with their self-efficacy.
A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image

ABSTRACT The understanding of mangrove forest structure and dynamics at species level is essential for mangrove conservation and management. To classify mangrove species, remote-sensing technologies provide a better way with high spatial resolution image. The spatial structure is usually viewed as effective complementary information for classification. However, it is still a challenge to design handcrafted features for mangrove species due to their non-structure texture. To leverage the advantage of convolutional neural networks (CNNs) in abstract feature exploration, a small patch-based CNN is proposed to overcome the requirement of fixed and large input which limits the applicability of CNNs to fringe mangrove forests. The function of down-sampling technology was substantially reduced to make deeper network for small input in our work. Meanwhile, the inception structure is used to exploit the multi-scale features of mangrove forests. Furthermore, the network is optimized with lesser convolution kernels and a single fully connected layer to reduce overfitting via reducing the training parameters. Compared to the features of grey level co-occurrence matrix with support vector machine, our proposed CNN shows better performance in classification accuracy. Moreover, the differences between mangrove species can be perceptive via CNN visualization, which offers better understanding of mangrove forests.
Does Social Sensitivity Impact Virtual Teams?

Working effectively as part of a team is an incredibly important skill to learn. Most jobs require interacting and working with others and the outcome of a team project relies on the processes of the group members. Naturally, a positive team experience yields better results than a negative experience. The biggest indicators of a positive team experience are communication and collaboration, which are both soft skills associated with the social sensitivity of the group members. Previous research has found that social sensitivity influences the amount of communication and the ultimate performance of traditional face-to-face teams. However, virtual teamwork is becoming more common. Does social sensitivity have a similar influence in virtual teams? Virtual teams have different interactions than face-to-face teams because they use communication technologies, which can hinder certain human cognitive processes. Our current research focuses on the influence of social sensitivity on the amount of communication and the performance of students who work as a virtual team using text-based communication in Discord. We find that teams with higher average levels of social sensitivity communicate more and perform better, even if they work in a virtual environment using only text messaging.
Using Essence in a Software Development Methodologies Course: An Experience Report

We report on our experience of using Essence as an educational tool in a course on Software Development Methodologies. Students used both the kernel and the language of Essence to discuss processes and endeavours, to define and combine practices, and to plan a Scrum-like process that was then applied in a workshop to build a Lego city. We found that using Alpha State Cards and the associated games helped students get a deeper understanding of the coherence of process elements and that the shared terminology was helpful in discussions. However, we observed students struggling with the idea of a language to describe processes and with what constitutes the kernel.
An Efficient Collision Power Attack on AES Encryption in Edge Computing

Edge computing has become a promising paradigm for the context-aware and delay-sensitive IoT data analytics. For the sake of security, some cryptographic algorithms such as AES, RSA, and so on, are employed for the encryption communication and authentication. The collision power attack is a typical physical attack to recover the secret key of the AES algorithm. However, almost all collision attacks aim at the detection of internal collisions caused by the output of S-boxes, and the linear layers are not concerned with those protected implementations. The relation between the mask and the masked data has been given little attention and stays as is, where the leakages still exist. In this paper, we focus on three typical AES implementations in edge computing, and propose a new type of collision attack by making use of leakages from linear layers, which is capable of breaking masking schemes with uniformly distributed random masks. In addition, a novel scalable collision attack of general applicability and high-efficiency is proposed and applied to masked linear layers and masked S-boxes. It can reach an equal level of performance compared to the second-order power analysis with acceptable off-line search, which improves the known collision attacks significantly.
A Bandwidth-Aware Authentication Scheme for Packet-Integrity Attack Detection on Trojan Infected NoC

Bandwidth is a fundamental issue in the NoC (Network on Chip) design and increasing use of the third-party NoC IPs adds the security as another design concern. The third-party NoC may have hardware Trojans that can break the confidentiality and integrity of the data transferred over the NoC, thereafter exposing the system to varied attacks. Basically, encryption can be applied to protect the data confidentiality; For the data integrity, authentication is often used. A general authentication approach for network communication is using a tag to identify the data; The tag is attached to the data at the source and the data is verified against the tag at the destination. For the effectiveness of the authentication, a large tag size is desired. But large tags, when transferred over the NoC, will consume considerable network bandwidth, potentially conflicting with the system performance requirement. In this paper, we propose a progressive packet data authentication scheme, where a packet is progressively authenticated against a set of small tag segments such that the authentication overhead is low yet the effective tag size is large. The experiments in our case study of 2D mesh NoC demonstrate that our design can achieve savings about 36% on bandwidth and 56% on implementation area cost as compared to the baseline design with the traditional non-progressive authentication scheme.
Adaptive Scheduling of Collocated Applications Using a Task-Based Runtime System

Task-based runtime systems are considered as one of the options for dealing with the challenges of upcoming parallel architectures. The greater flexibility of these runtime systems can also be used to dynamically adjust the resources allocated to the applications, adapting to the current load of the system and the progress of the applications. In our work, we have extended our implementation of the Open Community Runtime to support dynamic adjustment of execution threads. The runtimes communicate with an agent process, which collects performance data, computes thread allocation, and instructs the runtimes to make the required adjustments. We have tested our solution under different scenarios, focusing on producer-consumer applications, where the dynamic resource management was used to keep the applications in sync, improving the overall performance in some cases.
Extruding building footprints to create topologically consistent 3D city models

One of the simplest methods to construct a 3D city model is to extrude building footprints, to obtain "block-shaped" buildings. While the method is well-known and easy to implement, if the topological relationships between the footprints are not taken into account, the resulting city models will not necessarily be topologically consistent. As a result, the model will be of little use for most applications, besides visualisation that is. In this paper, we present a new extrusion algorithm to construct topologically correct 3D city models. It is conceptually simple and permits us to create city models in different formats (e.g. CityGML). We have implemented the algorithm, tested it for the creation of the model of our university campus and validated it by constructing the constrained Delaunay tetrahedralization.
Relaxation "sweet spot" exploration in pantophonic musical soundscape using reinforcement learning

Musical relaxation is a common method to relieve personal stress. Particularly, nature sounds, instrumental music, voice (chanting), "easy listening" songs, etc. can be played for relaxation. Nevertheless, effectiveness of the sounds used for the relaxation is idiosyncratic, depending on personal taste. In our approach, computer-guided audition for spatial soundscapes is investigated, automatically exploring a polyphonic area while using biosignals as indicators of satisfaction. We propose a reinforcement learning (RL) method to discover the sound relaxation "sweet spot" in a polyphonic soundscape. An avatar roams within a pantophonic space, surrounded by six independent audio channels, while a human subject, listening through the avatar's ears, is connected to an electroencephalographic (EEG) headset. Besides the position of the avatar, pitch, reverberation, and filters can also be changed to find the most relaxing virtual standpoint and parameters for the listener. Instead of changing position manually, a Deep Q-Network (DQN) in reinforcement learning is used. An RL agent adjusts parameters according to reward values calculated by change of relative theta band (4--8 Hz) power.
Approximate Q-Learning for Stacking Problems with Continuous Production and Retrieval

ABSTRACT This paper presents for the first time a reinforcement learning algorithm with function approximation for stacking problems with continuous production and retrieval. The stacking problem is a hard combinatorial optimization problem. It deals with the arrangement of items in a localized area, where they are organized into stacks to allow a delivery in a required order. Due to the characteristics of stacking problems, for example, the high number of states, reinforcement learning is an appropriate method since it allows learning in an unknown environment. We apply a Sarsa algorithm to real-world problem instances arising in steel industry. We use linear function approximation and elaborate promising characteristics of instances for this method. Further, we propose features that do not require specific knowledge about the environment and hence are applicable to any stacking problem with similar characteristics. In our experiments we show fast learning of the applied method and it’s suitability for real-world instances.
Encoder-Decoder Neural Networks for Taxonomy Classification

This paper describes our taxonomy classifier for SIGIR eComRakuten Data Challenge.We propose a taxonomy classifier based on sequenceto-sequence neural networks, which are widely used in machine translation and automatic document summarization, by treating taxonomy classification as the translation problem from a description of a product to a category path. Experiments show that our method can predict category paths more accurately than baseline classifier. CCS CONCEPTS • Computing methodologies→ Information extraction;