Reviewerly/exHarmony huggingface dataset:
A Survey on Despeckling Filters for Speckle Noise Removal in Ultrasound Images

In ultrasound images, speckle noise emerges as the major cause, which has the tendency to limit the contrast resolution. This affects the detection of low contrast and small lesions. To improve clinical diagnosis, speckle reduction filters are designed to enhance the visual quality as well as preserve important features in ultrasound images. Ultrasound imaging is mainly utilized for the detection of thyroid diseases. Several despeckling filters have been proposed for the reduction of speckle noise in ultrasound images. Despecking filters are based on statistics filtering, transform domain filtering, fuzzy logic filtering and partial differential equation based filtering. This paper presents an exhaustive search in literature on the existing methods for speckle reduction in ultrasound images.
A fuzzy interaction scheme of mid-air gesture elicitation

In a virtual assembly scenario, a semantic model of mid-air gesture interaction is established through a user-defined elicitation experiment. Considering the spatial-temporal continuity of the gesture movement, a fuzzy interaction scheme of gesture segments is proposed based on Trie Tree and Levenshtein distance. The experiment result proves that this design can effectively alleviate the effect of missing input sequences and recognition errors. Moreover, it helps relieve the memory load for users by establishing a close correlation of input actions.
An On-Demand Scalable Model for Geographic Information System (GIS) Data Processing in a Cloud GIS

With the rapid development of information processing technology and continuously evolving technological hardware and software, the scale of spatial data has grown exponentially. This has necessitated that stricter requirements be placed on the way data is processed. The traditional single-machine centralized data processing method is gradually being replaced by the network-based cloud geographic information system (GIS) mode. However, the information processing method of the business system in the existing spatial information network environment is difficult to expand, which limits the application of the business system. This paper proposes an on-demand and extended model for the GIS data processing procedure that considers the three-way separation of algorithm development, business processes, and the operational interface, and can implement an on-demand expansion of business processes by process modeling the business and task scheduling the workflow engine. This can expand the scope of business systems and improve the efficiency of business system construction.
Comparisons of Visual Activity Primitives for Voice Activity Detection

Voice activity detection (VAD) with solely visual cues have usually performed by detecting lip motion, which is not always feasible. On the other hand, visual activity (e.g., head, hand or whole body motion) is also correlated with speech, and can be used for VAD. Convolutional Neural Networks (CNNs) have demonstrated significantly good results for many applications including visual activity-related tasks. It can be possible to exploit CNN’s effectiveness to visual-VAD when whole body visual activity is used. The way visual activity is represented (called visual activity primitives) to be given to a CNN as input, might be important to perform an effective VAD. Some primitives might result in better detection and provide consistent VAD performance such that the detector works equally well for all speakers. This is investigated, for the first time, in this paper. Regarding that, we compare visual activity primitives quantitatively in terms of the overall performance and the standard deviation of the performance, and qualitatively by visualizing the discriminative image regions determined by CNN trained to identify VAD classes. We perform a data-driven VAD with a person-invariant training i.e., without using any labels or features of the test data. This is unlike the state-of-the-art (SOA), which realizes a person-specific VAD with hand-crafted features. Improved performances with much lower standard deviation as compared to SOA are demonstrated.
Human Detection and Tracking for Autonomous Human-following Quadcopter

In this project, a quadcopter which can autonomously detect and track a person using Deep Learning algorithm and correlation filter tracking technique on Raspberry Pi is presented. There are two major tasks for autonomous detection and tracking procedure executed by the quadcopter: First, a Deep Learning object detection CNN-based model named MobileNetv2-SSDLite was applied to detect a person. This model is proved to be efficient and fast for embedded system and mobile devices. The MobileNetv2-SSDLite model was originally trained on Common object in context dataset (COCO), we kept the weights in the Feature extractor layers (MobileNetsv2) and fine-tuned the detection layers (SSDLite). The purpose of this re-training task was to specialize the model on human detection and the evaluation after training were 98.6% for mAP@[0.5]IoU and 93.6% for mAP@[0.75]IoU. We used a vision-based tracking method for human tracking task named MOSSE to work along with the detection task. This technique uses the bounding box's coordinates collected from the detection task as the initial learning sample and consecutively updated new person's coordinate by online learning method. MOSSE tracking algorithm can also help with small occlusion due to the online-learning ability. Usually, running a Deep learning model like Object detection costs a lot of computational power, especially for an embedded computer like a Raspberry Pi. However, our work keeps the processing speed of the whole system fast enough for real-time implementation by combining an expensive Deep learning model with an inexpensive image-processing based tracking technique. Our proposed method can achieve 3 to 4 FPS on Raspberry Pi which is faster than 0.63 FPS of the detection algorithm. The mentioned results in this article were carried out by testing in a real-time environment with a self - developed quadcopter model.
Color Image Encryption Based on PC-MLCA and 3-D Chaotic Cat Map

With the emergence of cloud and social networking sites, information security has become a major challenge. Images are the most searched, uploaded and shared data in multimedia. However, because of the data size and high redundancy among the raw pixels of a digital image, traditional encryption algorithms might not be suitable for image encryption. In this paper, we propose a novel color image encryption algorithm using a three dimensional chaotic cat map and a programmable complemented maximum length CA(PC-MLCA). The proposed method performs a thorough experiment test through detailed analysis to demonstrate the high security and reliability of the new image encryption system.
Epistemic Sets Applied to Best-of-n Problems

Epistemic sets are a simple and efficient way of representing uncertain beliefs in AI, in which an agent identifies those states or worlds that she deems to be possible. We investigate their application to multi-agent distributed learning and decision making, in particular to best-of-n problems in which a population of agents must reach a consensus by identifying the best out of n possible alternatives or choices, each of different quality. We show that, despite their limited representational power, epistemic sets can be effectively deployed by agents engaged in a learning process in which they receive evidence directly from the environment and also pool or fuse their beliefs with those of other agents, in order to solve a best-of-n problem. We describe an analytical model of such a system based on ordinary differential equations and conduct a fixed point analysis so as to obtain insights into macro-level convergence properties. We then conduct a series of agent-based simulation experiments to investigate the robustness of the epistemic set approach. The results suggest that when applied to best-of-n problems epistemic sets are robust to noise and scalable to large state spaces, even when the population size is relatively small. This in turn supports the claim that they have potential applications in decentralised AI and swarm robotics at a range of different scales.
Design and Analysis of Ring Oscillator Influenced Beat Frequency Detection for True Random Number Generation on FPGA

True Random Number Generators (TRNGs) became the basic building block of modern cryptography. Though most of the noise source of TRNGs is analog circuit, to provide compactness and high throughput, hardware based TRNG solutions are recommended. This work aims at the generation of true random bits through beat frequency detection influenced by identical length of ring oscillators on FPGA. The main idea of this work is to extract the true randomness from one jitter clock produced by a ring oscillator through another jitter clock generated by the second ring oscillator. To enhance the randomness, Von Neumann Corrector (VNC) post processing is utilized. Proposed TRNG is designed using VHDL and Quartus II 8.0 EDA tool. It consumes 523 combinational functions and 539 logic registers on Altera Cyclone II EP2C20F4S4C7 FPGA where it achieves the throughput of 26.640650 Mbps when 27 MHz is used as sampling clock. Statistical efficiency of the TRNG is evaluated using entropy, correlation and NIST SP 800 -22 analyses. True randomness of the TRNG is verified through restart experiment.
An Augmented Reality App for Therapeutic Education and Suitable for Mobile Devices with Different Features

Patients with chronic diseases can improve their quality of life with therapeutic education. Augmented Reality (AR) can be used to develop therapeutic tools. This paper presents a study in which an AR app to support therapeutic education in diabetes was used. The app supports the learning about carbohydrate portions contained in different foods. The virtual food is shown on a real dish. Sixty-six children with diabetes participated in our study. The children were divided into three groups that used three different mobile devices. The main differences between the three devices were camera resolution and display size. We used two tablets with similar display size and different camera resolution (2-megapixel and 8-megapixel), and a smartphone with about half of the diagonal display size of the tablets and with an 8-megapixel camera. The results indicated that the children learned about carbohydrate estimation by using our app. There were no statistically significant differences for the knowledge acquired, or for the perceived usability and satisfaction among the three groups. This result points out that our AR app is effective as a therapeutic education tool independently of the features of the device used.
Controlling steady-state entanglement and quantum discord through squeezing angle

We analyze the dynamics of entanglement and quantum discord in a two-qubit system coupled to squeezed reservoirs. We present analytical expressions that can be used to efficiently calculate the evolution of quantum discord and entanglement between the two qubits. Our results show that quantum discord is a more general and robust measure of quantum correlations than entanglement. Furthermore, there is a relationship between the time before sudden death of entanglement and the reservoir squeezing angle. Additionally, the increase of squeezing angle accelerates the decay of quantum discord. These results provide new avenues for controlling quantum correlations and designing quantum states that can be used for various quantum computing applications.
A Complex Polyalphabetic Cipher Technique Myanmar Polyalphabetic Cipher

The rapid development of electronic exchange of digital information increases the importance of information security in storage and transmission of data on public communication network. Cryptography has emerged as a solution that plays an key role in the security of information against malicious attacks. There are various cipher techniques in cryptography, such as monoalphabetic cipher, polyalphabetic cipher, etc. to support data confidentiality as security mechanisms. They are methods of enveloping plain text message into cipher text protecting it from adversaries. The process of encryption of alphabets is the converting original message into non readable form. The Vigenère cipher is one of the most common basic cipher techniques. It is a polyalphabetic cipher technique which uses the Vigenère table for the process of enveloping English alphabets. But Vigenère cipher is longer vulnerable to Kasiski and Friedman attacks based on letter frequency analysis. Thus, in this paper we propose a polyalphabetic cipher that is a new encryption and decryption technique with diffusion and confusion properties based on the concept of the complex cipher used by combining of Vigenère cipher with Affine cipher for the increase of data security in data storage and transmission on public communication networks. Then, our proposed technique is extended by designing based on Myanmar alphabet characters for the increase of data security for Myanmar language. This cipher technique transforms the plain text message in Myanmar alphabet characters into the cipher text in Myanmar alphabet characters using the key in Myanmar alphabet characters.
Inspection guidelines to identify security design flaws

Recent trends in the software development practices (Agile, De-vOps, CI) have shortened the development life-cycle causing the need for efficient security-by-design approaches. In this context, software architectures are analyzed for potential vulnerabilities and design flaws. Yet, design flaws are often documented with natural language and require a manual analysis, which is inefficient. Besides low-level vulnerability databases (e.g., CWE, CAPEC) there is little systematized knowledge on security design flaws. The purpose of this work is to present and evaluate a catalog of security design flaws accompanied by inspection guidelines for their detection. To this aim, we conduct empirical studies with master and doctoral students. This paper presents a catalog of 19 inspection guidelines for detecting security design flaws and contributes with an empirical evaluation of the inspection guidelines. We also account for the shortcomings of the inspection guidelines and make suggestions for their improvement with respect to the generalization of guidelines, catalog re-organization, and format of documentation. We record similar precision, recall, and productivity in both empirical studies.
Multi-Objective Based Integrated Task Scheduling In Cloud Computing

Task scheduling problem in cloud computing can be seen as optimal assignment of tasks over available set of resources. Cloud Computing resources are heterogeneous, distributed and dynamic in nature. Therefore Scheduling in cloud computing is the NP-hard category of problem. Optimal solution is difficult to achieve while considering task scheduling with multiple conflicting objectives. The proposed work presents a novel scheduling algorithm called multi-objective based Integrated Task scheduling in cloud computing which aims for solving task scheduling problem of cloud computing. The goal of the proposed work is to provide a solution that is based on the multiple objectives execution time, execution cost and load balancing while aiming that each objective is satisfied equally and no single objective dominates the solution. The result obtains show that the proposed approach presents improvement in CPU utilization and reduction in make span parameters.
Training Efficient Semantic Segmentation CNNs on Multiple Datasets

In the past few years, various datasets for semantic segmentation have been presented. However, dense per-pixel groundtruths are difficult and expensive to obtain, therefore every single dataset contains only a subset of the semantic classes required to fully understand outdoor environments for real-world applications, e.g. autonomous or assisted driving. In this work, we investigate a simple approach to modify semantic segmentation CNNs in order to train them on multiple datasets with heterogeneous groundtruths. We trained and tested six efficient Deep CNN models on three datasets with different types of annotations such as generic objects, traffic signs and lane markings. Experiments show that the networks are trainable with the implemented method even though it highlights the limit of current efficient architectures when dealing with heterogeneous and large datasets.
Enhancing Home security systems Using IOT

This paper deals with the security system which has been designed for the old, physically challenged/disabled people and small children in home. Home security systems and smart homes has come very much into practice now a days due to its various advantages in day to day life. This can be done by changing the wired system to wireless systems and through this we can control the entire system through our android devices using Bluetooth connections. This paper presents a very low budget system which can be implemented in our home system by just using a Bluetooth device on our smart phones to connect to the home systems through applications by either using eye/face scanners, body/hand gestures or voice control in our systems. Through this our system becomes more secure and easy to handle for all aged peoples. This paper present how it can be so easy to use the system for even the people suffering from paraplegia who can use or get entry through the home systems by eye/face scanners or even the handprint scanners. I have used Bluetooth for a wireless communication as it is easily available and can be very easily handled by users.
Building Conversational Interface for Customer Support Applied to Open Campus an Open Online Course Provider

A conversational interface is an application that interacts with users using natural language in a similar way to imitate a technical support representative. The goal is to minimize customer service costs and ensure users get common, cheaply and fast responses. In this work, we presented a live customer service using a chatbot along with several services. The chatbot is applied to Open Campus, an open and free online course provider. The architecture proposed, if successful, could have widespread application in universities, and Open Educational Resources and MOOCs providers.
Two-step constructive approaches for dungeon generation

This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2. Generation is split into two steps, initially producing the architectural layout of the level as its walls and floor tiles, and then furnishing it with game objects representing the player's start and goal position, challenges and rewards. Three layout creators and three furnishers are introduced in this paper, which can be combined in different ways in the two-step generative process for producing diverse dungeons levels. Layout creators generate the floors and walls of a level, while furnishers populate it with monsters, traps, and treasures. We test the generated levels on several expressivity measures, and in simulations with procedural persona agents.
Identity Recognition Based on Face Image

Abstract This article is aimed at designing a human identity recognition algorithm based on face image, which will be used in indoor environment. As the working environment is set as indoor environment, the camera will not be affected much by illumination variation. The key is how to detect human face and handle the variation of facial pose. This article divides the whole recognition process into 4 parts: image pre-processing, face detection, face alignment, feature extraction and comparison. Face detection and feature extraction are the core functions and both realized by deep learning. The process of the whole algorithm can be described as following: images after pre-processing are fed to face detection network to get the locations of face and face landmarks. Then face alignment will be conducted. Finally, deep features of face will be extracted and compared. The unique features of this algorithm are its good performance of handling the variation of facial pose and its clear framework which allows the whole method can be easily adjusted and upgraded.
Refine and Recycle: A Method to Increase Decompression Parallelism

Rapid increases in storage bandwidth, combined with a desire for operating on large datasets interactively, drives the need for improvements in high-bandwidth decompression. Existing designs either process only one token per cycle or process multiple tokens per cycle with low area efficiency and/or low clock frequency. We propose two techniques to achieve high single-decoder throughput at improved efficiency by keeping only a single copy of the history data across multiple BRAMs and operating on each BRAM independently. A first stage efficiently refines the tokens into commands that operate on a single BRAM and steers the commands to the appropriate one. In the second stage, a relaxed execution model is used where each BRAM command executes immediately and those with invalid data are recycled to avoid stalls caused by the read-after-write dependency. We apply these techniques to Snappy decompression and implement a Snappy decompression accelerator on a CAPI2-attached FPGA platform equipped with a Xilinx VU3P FPGA. Experimental results show that our proposed method achieves up to 7.2 GB/s output throughput per decompressor, with each decompressor using 14.2% of the logic and 7% of the BRAM resources of the device. Therefore, a single decompressor can easily keep pace with an NVMe device (PCIe Gen3 x4) on a small FPGA, while a larger device, integrated on a host bridge adapter and instantiating multiple decompressors, can keep pace with the full OpenCAPI 3.0 bandwidth of 25 GB/s.
A Bounded Scheduling Method for Adaptive Gradient Methods

Many adaptive gradient methods have been successfully applied to train deep neural networks, such as Adagrad, Adadelta, RMSprop and Adam. These methods perform local optimization with an element-wise scaling learning rate based on past gradients. Although these methods can achieve an advantageous training loss, some researchers have pointed out that their generalization capability tends to be poor as compared to stochastic gradient descent (SGD) in many applications. These methods obtain a rapid initial training process but fail to converge to an optimal solution due to the unstable and extreme learning rates. In this paper, we investigate the adaptive gradient methods and get the insights on various factors that may lead to poor performance of Adam. To overcome that, we propose a bounded scheduling algorithm for Adam, which can not only improve the generalization capability but also ensure the convergence. To validate our claims, we carry out a series of experiments on the image classification and the language modeling tasks on several standard benchmarks such as ResNet, DenseNet, SENet and LSTM on typical data sets such as CIFAR-10, CIFAR-100 and Penn Treebank. Experimental results show that our method can eliminate the generalization gap between Adam and SGD, meanwhile maintaining a relative high convergence rate during training.