Reviewerly/exHarmony huggingface dataset:
Learning to Extract and Use ASNs in Hostnames

We present the design, implementation, evaluation, and validation of a system that learns regular expressions (regexes) to extract Autonomous System Numbers (ASNs) from hostnames associated with router interfaces. We train our system with ASNs inferred by Router-ToAsAssignment and bdrmapIT using topological constraints from traceroute paths, as well as ASNs recorded by operators in PeeringDB, to learn regexes for 206 different suffixes. Because these methods for inferring router ownership can infer the wrong ASN, we modify bdrmapIT to integrate this new capability to extract ASNs from hostnames. Evaluating against ground truth, our modification correctly distinguished stale from correct hostnames for 92.5% of hostnames with an ASN different from bdrmapIT's initial inference. This modification allowed bdrmapIT to increase the agreement between extracted and inferred ASNs for these routers in the January 2020 ITDK from 87.4% to 97.1% and reduce the error rate from 1/7.9 to 1/34.5. This work opens a broader horizon of opportunity for evidence-based router ownership inference.
iMAC: Implicit Message Authentication Code for IoT Devices

The role of cryptography is not only limited to enabling secure communication but also providing mechanisms for authentication, integrity, privacy, trust and non-repudiation. All these mechanisms play vital role in modern digital communications. Currently, number of interconnected IoT devices has exceeded over billions, which makes managing them not only challenging but also impractical. Central management of IoT devices also introduces serious security threats such as central point of failure and lack of transparency, confidentiality, and integrity. Lightweight and decentralized device-to-device schemes are important to cope with the ever increasing number of resource constrained IoT devices. Implicit cryptographic schemes, in which only devices in the same context can authenticate and communicate, is an efficient way to achieve decentralized and transparent IoT security. In this paper, we propose a multi-sensor-based lightweight implicit message authentication mechanism. The proposed scheme is efficient and secure.
Feature Extraction with Video Summarization of Dynamic Gestures for Peruvian Sign Language Recognition

In peruvian sign language (PSL), recognition of static gestures has been proposed earlier. However, to state a conversation using sign language, it is also necessary to employ dynamic gestures. We propose a method to extract a feature vector for dynamic gestures of PSL. We collect a dataset with 288 video sequences of words related to dynamic gestures and we state a workflow to process the keypoints of the hands, obtaining a feature vector for each video sequence with the support of a video summarization technique. We employ 9 neural networks to test the method, achieving an average accuracy ranging from 80% and 90%, using 10 fold cross-validation.
Identification of Construction Era for Indian Subcontinent Ancient and Heritage Buildings by Using Deep Learning

The Indian subcontinent is a south geographic part of Asia continent which consists of India, Bangladesh, Pakistan, Sri Lanka, Bhutan, Nepal, and Maldives. Different rulers or the empire of different periods have built various buildings and structures in these territories like Taj Mahal (Mughal Period), Sixty Dome Mosque (Sultanate Period), etc. From archaeological perspectives, a computational approach is very essential for identifying the construction period of the old or ancient buildings. This paper represents the construction era or period identification approach for Indian subcontinent old heritage buildings by using deep learning. In this study, it has been focused on the constructional features of British (1858–1947), Sultanate (1206–1526), and Mughal (1526–1540, 1555–1857) periods’ old buildings. Four different feature detection methods (Canny Edge Detector, Hough Line Transform, Find Contours, and Harris Corner Detector) have been used for classifying three types of architectural features of old buildings, such as Minaret, Dome and Front. The different periods’ old buildings contain different characteristics of the above-mentioned three architectural features. Finally, a custom Deep Neural Network (DNN) has been developed to apply in Convolutional Neural Network (CNN) for identifying the construction era of above-mentioned old periods.
Global Positioning System: Monitoring the Fuel Consumption in Transport Distribution

Nowadays, most of the organizations are looking forwards to achieve optimization in their operation and productivity. With the aid of modern transport technology applications, it can create better visibility within the operation, which will enable more control over the business and stay ahead of the competition. Currently, most of the Road Haulage company has started to use the Global Positioning System (GPS) as a trucking system to strengthen its distribution operation. By implementing this GPS application, some of the questions will arise on the effectiveness of this technology implementation. Therefore, this paper has focused on monitoring the fuel consumption by the company that implementing this technology in their transport distribution operation. The researchers used two data collection techniques which are through site monitoring and also the structured interview. The main focus of the analysis is to compare the usage of fuel after implementing this system. This study is one of the information platforms for the logistics and transportation industry to grow and propels the country into a high-income economy based on the 11th Malaysia Plan.
Castle in the Sky: Dynamic Sky Replacement and Harmonization in Videos

We propose a vision-based framework for dynamic sky replacement and harmonization in videos. Different from previous sky editing methods that either focus on static photos or require real-time pose signal from the camera's inertial measurement units, our method is purely vision-based, without any requirements on the capturing devices, and can be well applied to either online or offline processing scenarios. Our method runs in real-time and is free of manual interactions. We decompose the video sky replacement into several proxy tasks, including motion estimation, sky matting, and image blending. We derive the motion equation of an object at infinity on the image plane under the camera's motion, and propose "flow propagation", a novel method for robust motion estimation. We also propose a coarse-to-fine sky matting network to predict accurate sky matte and design image blending to improve the harmonization. Experiments are conducted on videos diversely captured in the wild and show high fidelity and good generalization capability of our framework in both visual quality and lighting/motion dynamics. We also introduce a new method for content-aware image augmentation and proved that this method is beneficial to visual perception in autonomous driving scenarios. Our code and animated results are available at https://github.com/jiupinjia/SkyAR.
Context-Dependent Feature Values in Text Categorization

Feature engineering is one aspect of knowledge engineering. Besides feature selection, the appropriate assignment of feature values is also crucial to the performance of many software applications, such as text categorization (TC) and speech recognition. In this work, we develop a general method to enhance TC performance by the use of context-dependent feature values (aka term weights), which are obtained by a novel adaptation of a context-dependent adjustment procedure previously shown to be effective in information retrieval. The motivation of our approach is that the general method can be used with different text representations and in combination of other TC techniques. Experiments on several test collections show that our context-dependent feature values can improve TC over traditional context-independent unigram feature values, using a strong classifier like Support Vector Machine (SVM), which past works have found to be hard to improve. We also show that the relative performance improvement of our method over the context-independent baseline is comparable to the levels attained by recent word embedding methods in the literature, while an advantage of our approach is that it does not require the substantial training needed to learn word embedding representations.
Transitioning to a Large-Scale Distributed Programming Course

The COVID-19 pandemic has challenged our day-today life, businesses, and educational institutions by changing the way we interact with each other in a very short space of time. In the context of teaching, lecturers had to rapidly develop concepts and teaching materials that enable distributed virtual and safe learning experiences. In this paper, we describe the challenges of reorganizing a two-week programming course that we have taught for more than ten years in a classroom setting using face-to-face communication. The course teaches students the basics of developing a software system using the Swift programming language and is a prerequisite for a single semester capstone course. We show how we reworked the course into a distributed format using online sessions and real-time feedback for about 80 students. We describe the remote supervision approaches we used to support students and the process of rethinking the course infrastructure by enabling remote access as well as by offering a semi-automated merge management and code review system. Based on this experience, we provide instructors with insights on how to set up and conduct a distributed software engineering course when face-to-face teaching is impossible.
Muse

Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends on high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing event trend aggregation poses several technical challenges. First, Kleene patterns are in general difficult to share due to complex nesting and arbitrarily long matches. Second, not all sharing opportunities are beneficial because sharing Kleene patterns incurs non-trivial overhead to ensure the correctness of final aggregation results. We propose MUSE (Multi-query Shared Event trend aggregation), the first framework that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. To find the beneficial sharing plan, the MUSE optimizer effectively selects robust sharing candidates from the exponentially large search space. Our experiments demonstrate that MUSE increases throughput by 4 orders of magnitude compared to state-of-the-art approaches.
The Social Market Economy Model in Africa: A Policy Lesson in the Pursuit of an Inclusive Development

A contextually rethought coexistence of capitalism and socialism, particularly, the ‘Third-Way’ politico-economic framework is a contemporary dominion in the pursuit of prosperous and inclusive development. Regarding the third-way position, however, there is a dearth of theoretical framework in African studies. Hence, this article aimed at exploring the theoretical significance of ordoliberalism and its social market economy model that is often praised as the secret(s) in the wake of the ‘Wirtschaftswunder’, meaning the ‘economic miracle’, of Germany. In so doing, the article has sought the common conceptual ground between the notions of the social market economy and inclusive development through the extensive review of theoretical evidence available in the secondary sources of data. The review of literature has revealed that unlike the German experience, the policy choice among the African countries, in the post-colonial era, was never consistent with ordoliberalism or social market economy. However, the post-2000 economic trajectory of Africa has shown the coexistence of the welfare state and coordinated market thereby creating a convenient condition to implement the lessons learned from the development path of Germany. Besides, the prevalent socio-economic problems in most of the Sub-Saharan African countries including demographic bulge, abject poverty, high levels of income inequality, extractive/rent-seeking institutions of governance, brain-drain, and aid/loan dependency syndrome are the major factors that underline the urgency for policy reforms geared towards an Afro-centric social market economy. Yet, the levels of economic development, historical, cultural, and geopolitical differences need to be taken into account to effectively implement the policy instruments of the social market economy in Africa.
Entanglement detection in triangle-free quantum states

We present an alternative approach to unveil a different kind of entanglement in bipartite quantum states whose diagonal zero patterns in suitable matrix representations admit a nice description in terms of triangle-free graphs. Upon application of a local averaging operation, the separability of such states transforms into a simple matrix positivity condition, the violation of which implies the presence of entanglement. We completely characterize the class of triangle-free graphs, which allows for nontrivial entanglement detection using the above test. Moreover, we develop a recipe to construct a plethora of unique classes of positive partial transpose (PPT) entangled states in arbitrary dimensions. Finally, we link the task of entanglement detection in general states to the well-known graph-theoretic problem of finding triangle-free-induced subgraphs in a given graph.
JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels

Image generation with explicit condition or label generally works better than unconditional methods. In modern GAN frameworks, both generator and discriminator are formulated to model the conditional distribution of images given with labels. In this paper, we provide an alternative formulation of GAN which models the joint distribution of images and labels. There are two advantages in this joint formulation over conditional approaches. The first advantage is that the joint formulation is more robust to label noises if it's properly modeled. This alleviates the burden of making noise-free labels and allows the use of weakly-supervised labels in image generation. The second is that we can use any kinds of weak labels or image features that have correlations with the original image data to enhance unconditional image generation. We will show the effectiveness of our joint formulation on CIFAR10, CIFAR100, and STL dataset with the state-of-the-art GAN architecture.
LevelEd VR: A virtual reality level editor and workflow for virtual reality level design

Virtual reality entertainment and serious games popularity has continued to rise but the processes for level design for VR games has not been adequately researched. Our paper contributes LevelEd VR; a generic runtime virtual reality level editor that supports the level design workflow used by developers and can potentially support user generated content. We evaluated our LevelEd VR application and compared it to an existing workflow of Unity on a desktop. Our current research indicates that users are accepting of such a system, and it has the potential to be preferred over existing workflows for VR level design. We found that the primary benefit of our system is an improved sense of scale and perspective when creating the geometry and implementing gameplay. The paper also contributes some best practices and lessons learned from creating a complex virtual reality tool, such as LevelEd VR.
Guided optimization framework for the fusion of time-of-flight with stereo depth

The fusion of depth acquired actively with the depth estimated passively proved its significance as an improvement strategy for gaining depth. This combination allows us to benefit from two sources of modalities such that they complement each other. To fuse two sensor data into a more accurate depth map, we must consider the limitations of active sensing such as low lateral resolution while combining it with a passive depth map. We present an approach for the fusion of active time-of-flight depth and passive stereo depth in an accurate way. We propose a multimodal sensor fusion strategy that is based on a weighted energy optimization problem. The weights are generated as a result of combining the edge information from a texture map and active and passive depth maps. The objective evaluation of our fusion algorithm shows an improved accuracy of the generated depth map in comparison with the depth map of every single modality and with the results of other fusion methods. Additionally, a visual comparison of our result shows a better recovery on the edges considering the wrong depth values estimated in passive stereo. Moreover, the left and right consistency check on the result illustrates the ability of our approach to consistently fuse sensors.
DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices

In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem.
Short-term Solar Irradiance Evaluation and Modeling of a Hybrid Distribution Generation System for a Typical Nigeria University

Intermittency of renewable energy sources is of great concern to Researchers. For example, PV panels are limited due to the variability of solar irradiances. To further integrate PV into the overall energy mix, accurate assessment and evaluation of its performance is important. This paper aims to evaluate the potential and variability of solar energy at Afe Babalola University, also the possibility of incorporating PV system into the energy mix in ABUAD is investigated. To this end, measurements of solar irradiance (GHI) are collated from January 2018 to December 2018 using the Tenmars TM 206 Pyranometer. In addition, a hybrid system was modeled using HOMER, which gives an optimal system comprising of 1800kW PV panels, 1500kW diesel generator, 1200kW converter and four Trojan T-105 batteries with the levelized cost of energy (COE) of 0.719$/yr and Net Present Cost (NPC) of $41,676,188.
Carpe Diem, Seize the Samples Uncertain "at the Moment" for Adaptive Batch Selection

The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain samples predicted inconsistently in recent iterations. The historical label predictions of each training sample are used to evaluate its predictive uncertainty within a sliding window. Then, the sampling probability for the next mini-batch is assigned to each training sample in proportion to its predictive uncertainty. By taking advantage of this design, Recency Bias not only accelerates the training step but also achieves a more accurate network. We demonstrate the superiority of Recency Bias by extensive evaluation on two independent tasks. Compared with existing batch selection methods, the results showed that Recency Bias reduced the test error by up to 20.97% in a fixed wall-clock training time. At the same time, it improved the training time by up to 59.32% to reach the same test error.
BUDS: Balancing Utility and Differential Privacy by Shuffling

Balancing utility and differential privacy by shuffling or BUDS is an approach towards crowd sourced, statistical databases, with strong privacy and utility balance using differential privacy theory. Here, a novel algorithm is proposed using one-hot encoding and iterative shuffling with the loss estimation and risk minimization techniques, to balance both the utility and privacy. In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report. During empirical test of balanced utility and privacy, BUDS produces ε = 0.02 which is a very promising result. Our algorithm maintains a privacy bound of ε = ln[t/((n1-1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> )] and loss bound of c <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">'</sup> |e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ln[t/((n1-1)S</sup> )]-1|.
Design and implementation of power efficient image compressor for WSN systems

In the digital electronics era, video and image processing, storage, data transmitting, data streaming, data retrieval, video playback features increasing exponentially. These requirements data processing and storage improvement results to necessity of hybrid compression, efficient encoding and decoding, compression ratio techniques in demand for modern real time applications. For that spatial domine technology of compression is prefer to satisfy the computational constraint for real time gadgets. Discrete wavelet transformation (DWT) based Multi-Level Block Truncation Coding (MLBTC) methodology used for special domine. Discrete Wavelet Transform procedure is utilized to constant and motion images and implemented to all mean pixel estimation of the image. Loss type compression technology BTC is implemented for gray scale image processing. It designed that original image divided as square blocks with help of quantize for decrease the gray intensity images every portion of square box having equal standard deviation and Mean values. In this proposed methodology is analysis of BWT-MLBTC image compression technologies for both binary and color images. The methodology has been implemented in Xilinx ISE software, the simulation results shows that the proposed method consumes less hardware resources, delay generations and power utilizations compared to the state of art approaches.
New Efficient QERPKC based on Partial Discrete Logarithm Problem

In this study, our aim is to extend the scope for public key cryptography. We offered a new efficient public key encryption scheme using partial discrete logarithm problem (PDLP). It is known as the Quadratic Exponentiation Randomized Public Key Cryptosystem (QERPKC). Security of the presented scheme is based on the hardness of PDLP. We reflect the safety in contrast to trick of certain elements in the offered structure and demonstrated the prospect of creating an extra safety structure. The presented new efficient QERPKC structure is appropriate for low-bandwidth communication, low-storage and low-computation environments.