Reviewerly/exHarmony huggingface dataset:
Structural Low-Rank Tracking

Visual object tracking is an important step for many computer vision applications. The task becomes very challenging when the target undergoes heavy occlusion, background clutters, and sudden illumination variations. Methods that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. However, because of the lack of structural constraints, these methods show performance degradation when an object faces the aforementioned challenges. To alleviate these limitations, we propose a new structural low-rank modeling algorithm for robust object tracking. In the proposed algorithm, we enforce local spatial, global spatial and temporal appearance consistency among the particles in the low-rank subspace by constructing three graphs. The Laplacian matrices of these graphs are incorporated into the novel low-rank objective function which is solved using linearized alternating direction method with an adaptive penalty. Our proposed objective function jointly learns the spatial, global, and temporal structure of the target particles in consecutive frames and makes the proposed tracker consistent against many complex tracking scenarios. Results on two challenging benchmark datasets show the superiority of the proposed algorithm as compared to current state-of-the-art methods.
Enhancing object detection in the dark using U-Net based restoration module

In recent years, we have witnessed the widespread application of deep-learning techniques to various surveillance tasks, including human tracking and counting, abnormal behavior detection, and video segmentation. In most cases, the input images/videos are assumed to possess adequate visual quality to guarantee satisfactory performance. However, accuracy may be adversely affected when the input data are degraded by factors such as excessive noise or poor lighting conditions. In the paper, we develop a deep neural network based on the U-Net architecture that acts as a pre-processing module to restore images/videos with nonuniform light sources to ensure the accuracy of the subsequent object detection process. Experimental results on the VisDrone20 19 dataset [1] demonstrate the effectiveness of the proposed method, achieving a remarkable 5% increase in average recall. We expect the framework to be universally applicable to situations that call for the enhancement of raw input data.
Calibration of multiple cameras for large-scale experiments using a freely moving calibration target

Abstract Obtaining accurate experimental data from Lagrangian tracking and tomographic velocimetry requires an accurate camera calibration consistent over multiple views. Established calibration procedures are often challenging to implement when the length scale of the measurement volume exceeds that of a typical laboratory experiment. Here, we combine tools developed in computer vision and non-linear camera mappings used in experimental fluid mechanics, to successfully calibrate a four-camera setup that is imaging inside a large tank of dimensions $$\sim 10 \times 25 \times 6 \; \mathrm {m}^3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∼</mml:mo><mml:mn>10</mml:mn><mml:mo>×</mml:mo><mml:mn>25</mml:mn><mml:mo>×</mml:mo><mml:mn>6</mml:mn><mml:mspace /><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mn>3</mml:mn></mml:msup></mml:mrow></mml:math> . The calibration procedure uses a planar checkerboard that is arbitrarily positioned at unknown locations and orientations. The method can be applied to any number of cameras. The parameters of the calibration yields direct estimates of the positions and orientations of the four cameras as well as the focal lengths of the lenses. These parameters are used to assess the quality of the calibration. The calibration allows us to perform accurate and consistent linear ray-tracing, which we use to triangulate and track fish inside the large tank. An open-source implementation of the calibration in Matlab is available. Graphic abstract
Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud

The Artificial Intelligence BlockCloud (AIBC) is an artificial intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer (the latter three are the collective “upper-layers”). The AIBC layers have distinguished responsibilities and thus performance and robustness requirements. The upper layers need to follow a set of economic policies strictly and run on a deterministic and robust protocol. While the fundamental layer needs to follow a protocol with high throughput without sacrificing robustness. As such, the AIBC implements a two-consensus scheme to enforce economic policies and achieve performance and robustness: Delegated Proof of Economic Value (DPoEV) incentive consensus on the upper layers, and Delegated Adaptive Byzantine Fault Tolerance (DABFT) distributed consensus on the fundamental layer. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to enforce the DPoEV, as well as to achieve the best balance of performance, robustness, and security. The DPoEV-DABFT dual-consensus architecture, by design, makes the AIBC attack-proof against risks such as double-spending, short-range and 51% attacks; it has a built-in dynamic sharding feature that allows scalability and eliminates the single-shard takeover. Our contribution is four-fold: that we develop a set of innovative economic models governing the monetary, trading and supply-demand policies in the AIBC; that we establish an upper-layer DPoEV incentive consensus algorithm that implements the economic policies; that we provide a fundamental layer DABFT distributed consensus algorithm that executes the DPoEV with adaptability; and that we prove the economic models can be effectively enforced by AIBC’s DPoEV-DABFT dual-consensus architecture.
Underwater Image Processing System for Image Enhancement and Restoration

With the purpose of exploring the deep undersea world map, a high-quality image without interfering object is preferred. However, as in the water, the image quality tends to be impaired by water density, light attenuation, and scattering effect. Besides, the dynamic interference may affect the real underwater map. In this paper, we proposed a multi-step and all-round underwater image processing system specially for the underwater images taken in succession to improve the image quality, remove the dynamic interference and reconstruct the image. First of all, by implementing dark channel prior and improved grey world algorithm, image contrast is enhanced and color is corrected. Concerning image enhancement, firstly, the dynamic interference is recognized and removed. Secondly, we applied upgraded total variation model to patch the blank at the cost of resolution. Finally, super-resolution of the details is realized by applying improved BP network. After simulation and experiments, our system proved to achieve ideal results of image enhancement and reconstruction.
Leverage of Limb Detection in Pose Estimation for Vulnerable Road Users

Application of human pose estimation in intelligent driving has been gradually popular in recent years, as human pose could be informative in depiction of their actions and intentions. The focus of this paper is pose estimation for vulnerable road users with relatively small amounts of data. We present an improved framework, transferring general human pose estimation algorithms based on convolutional neural network to intelligent vehicle domain. Limb detection is introduced as an auxiliary task to allow explicit appearance-based modeling of spatial relationships among human keypoints. The network for keypoint detection is initialized with the parameters in limb detection task. Besides, the produced limb heatmaps are incorporated with keypoint heatmaps as extra source to gain the final keypoint locations. Concerning practical application, we adapt the widely used pose estimation networks to our proposed framework and evaluate them on the newly introduced TDCB-Pose dataset. The experiment results validate our method, and demonstrate the leverage of limb detection in human pose estimation without abundant data.
Fast BFS-Based Triangle Counting on GPUs

In this paper, we propose a novel method to compute triangle counting on GPUs. Unlike previous formulations of graph matching, our approach is BFS-based by traversing the graph in an all-source-BFS manner and thus can be mapped onto GPUs in a massively parallel fashion. Our implementation uses the Gunrock programming model and we evaluate our implementation in runtime and memory consumption compared with previous state-of-the-art work. We sustain a peak traversed-edgesper-second (TEPS) rate of nearly 10 GTEPS. Our algorithm is the most scalable and parallel among all existing GPU implementations and also outperforms all existing CPU distributed implementations. This work specifically focuses on leveraging our implementation on the triangle counting problem for the Subgraph Isomorphism Graph Challenge 2019, demonstrating a geometric mean speedup over the 2018 champion of 3.84×.
A novel crypto technique based ciphertext shifting

One of the significant issues in information security areas is a hidden exchange of data. There are several techniques for this purpose such as cryptography, steganography, etc. Generally, in cryptography, the secret message content is scrambled. In another hand in steganography, the secret message is embedded inside the cover medium. In this paper, a new crypto technique-based ciphertext shifting algorithm has been designed to improve the security for our previous work that combining cryptographic and steganographic. The improvement in the security of the secret message is done by changing the ciphertext value. The proposed shifting algorithm is used to rearranges the location of each character of ciphertext based on key value, in which the final ciphertext length is equivalent to encryption value but it different in value. The key strength of this method is two side one is trick the attacker from notice any change in ciphertext which is the same length as the original, so when used the common cryptanalysis will not get anything due to the original ciphertext has been changed. The second key strength of this method is that the shifting value is variable and dependent on key length. This method is inspected to be a very strong technique that can prevent common cryptography attacks such as a dictionary or brute-force attacks, etc.
Stepping Away From the Lamppost: Domain-Level Technical Debt

Background: Technical debt is a metaphor for trading software quality for business goals, reminding actors of the deferred cost associated with such trade-offs. The bulk of the literature on technical debt focuses on source code quality debt. Much less research is devoted to other forms of technical debt, such as documentation debt or architecture debt. In practice, however, we often observe technical debt pertaining to even higher-level, domain-oriented design issues. Goal: In this position paper, we aspire to bring attention to this gap in the study of technical debt and establish "domain debt" as a first class citizen in the discourse on technical debt. We aim to further this discourse by providing relevant examples and a working definition. Method: Based on many years of industrial experience, we present prototypical examples of domain debt from an industrial project, representing common design flaws of Enterprise Information Systems. We explore the origin and impact of these faults, and analyze the differences to other forms of technical debt. We use our examples to produce a working definition of the notion "domain debt". Conclusions: Domain debt is a useful notion in practice. It is suitable to steer the conversation around business trade-offs towards higher levels of system design quality. It has not been covered sufficiently in research so far, despite its relevance.
Business as Unusual: A Model for Continuous Real-Time Business Insights Based on Low Level Metrics

A wide variety of tools to monitor and track software systems, such as websites or smartphone applications, during runtime already exists. However, their aggregated results are often not sufficient to answer questions on a product management level since these questions address several levels of complexity and abstractions, and tend to be formulated on a rather high level, for instance concerning the efficiency of their website structure for their users. A straightforward mapping between low level metrics and high level insights is typically not possible. This causes a gap that makes it challenging to continuously provide quantitative high-level insights in real-time. In order to address this challenge, we conducted a study within three distinct platforms and products, and propose a model based on our results. After defining a case for each of the independent platforms and products, we implemented a process to measure high level insights using low level metrics for each of these cases. Next, we compared the procedures and steps that were taken in each of the cases and derived a model that describes a generic approach how to utilize and process data in order to gain higher level insights. Our model structures the steps from data to knowledge over different levels of complexity and abstraction, namely operational, tactical, and strategic. Thereby, the knowledge acquired in each phase serves as input in the next phase which increases the measurable level of complexity with each iteration. Since the steps in our model are specifically arranged as a pipeline, it enables practitioners to automate a continuous and quantitative measurement of high level insights in real-time.
How to Select Open Source Components

With millions of open source projects available on forges such as GitHub, it may be difficult to select those that best match your requirements. Examining each project’s product and development process can help you confidently select the open source projects required for your work.
When Englishes go digital

Abstract Digital communications have meant a significant acceleration in rate and a widening in scope of interactions across Englishes (Friedrich &amp; de Figueiredo, 2016). From social media posts to fan‐fiction writing, Englishes have been re‐negotiated, and new norms of interface and collaboration have been forged. Hybrid varieties have emerged, and new norms of politeness and opposition (Barnes, 2015) are constantly developing. For example, fan fiction can work as a channel for rewriting social norms through modification of existing texts (Leavenworth, 2015, p. 40), and each social media outlet has its own norms of accepted practices that give away who is an insider and who is not. Through an unstructured exploratory analysis, this paper discusses a few of the features and loci of these new modes of interaction (as in the delineation of speech community, conversational patterns, linguistic innovation) by visiting some of the media most affected by digital modes (such as social media, fan fiction communities, and texting). I will conclude with directions for further research and suggestions for pedagogical actions that not only prepare learners to deal with this reality but also take advantage of the skills they already intuitively bring into classrooms, virtual and physical.
Shallow SqueezeNext: An Efficient &amp; Shallow DNN

CNN has gained great success in many applications but the major design hurdles for deploying CNN on driver assistance systems or ADAS are limited computation, memory resource, and power budget. Recently, there has been greater exploration into small DNN architectures, such as SqueezeNet and SqueezeNext architectures. In this paper, the proposed Shallow SqueezeNext architecture for driver assistance systems achieves better model size with a good model accuracy and speed in comparison to baseline SqueezeNet and SqueezeNext architectures. The proposed architecture is compact, efficient and flexible in terms of model size and accuracy with minimum tradeoffs and less penalty. The proposed Shallow SqueezeNext uses SqueezeNext architecture as its motivation and foundation. The proposed architecture is developed with intention for implementation or deployment on a real-time autonomous system platform and to keep the model size less than 5 MB. Due to its extremely small model size, 0.370 MB with a competitive model accuracy of 82.44 %, decent both training and testing model speed of 7 seconds, it can be successfully deployed on ADAS, driver assistance systems or a real time autonomous system platform such as BlueBox2.0 by NXP. The proposed Shallow SqueezeNext architecture is trained and tested from scratch on CIFAR-10 dataset for developing a dataset specific trained model.
Embracing Quality with Design Thinking

Developed outcomes that depend upon software can be of such quality that end users do not merely accept the product or service, they embrace it. The product or service is of such a quality that end users are excited, inspired, motivated, committed, or possibly relieved to be using the software directly or indirectly (i.e., an embedded component). Achieving embracing quality requires understanding user needs and desires as well as their environmental contexts and accommodating these understandings within the development practice. Design thinking is one approach to deliver products and services grounded in a user-informed development process.
Building a Fuzzy Logic-Based Artificial Neural Network to Uplift Recommendation Accuracy

Abstract With the advent of the internet, the recommender system escorts the users in a customized way to nominate items from a massive set of possible alternatives. The emergence of overspecification in recommender system has emphasized negative effects on the context of prediction. The drift of user interest over time is one of the challenging affairs in present personalized recommender system. In this paper, we present a neural network model to improve the recommendation performance along with usage of fuzzy-based clustering to decide membership value of users and matching imputation to cutback sparsity to some extent. We evaluate our model on the MovieLens dataset and show that our model not only elevates accuracy, but also considers the order in which recommendation should be given. We compare the proposed model with a number of state-of-the-art personalization methods and show the dominance of our model using accuracy metrics such as root-mean-square error and mean absolute error.
Class Attendance Recording using QR Code via Smartphone

This article presents auto classroom attendance recording by using QR code on smartphones. Both teachers and students take responsible for their parts. The teachers evaluate the result of each subjects. Then, the codes for each will be generated. When the students receive the code, they can scan and the attendance will be recorded automatically. This research is divided into 3 modules; teacher module, generate module, and student module. This research found that this attendance record method resulted in a positive way. The result was evaluated by three experts (mean = 3.78; S.D. = 0.48). Also, the satisfaction rate was evaluated by ten users which resulted in `very satisfy' (mean = 4.45; S.D. = 0.27).
Bayesian Fuzzy Clustering neural network for regression problems

Smart models are responsible for solving complex problems within the routines of people and companies. The complexity of their structures can be defined by the methodologies used in the definition of parameters or architecture of their models. In three-layer fuzzy neural networks, the number of neurons is determined by fuzzification techniques capable of creating elements based on the data submitted to the training of the intelligent structures. The choice of the fuzzification method can make the model architecture complex and with many parameters. To create a more compact fuzzy neural network architecture, this paper presents a new fuzzy approach for fuzzy neural networks based on Bayesian clustering. This technique expands the training capabilities of the fuzzy c-means algorithm by adding to its methods the nature of the particle filter inference technique to estimate the model parameters including the number of clusters, making the approach more straightforward and less dependent on settings for the construction of the neural network fuzzy. The other layers of the model use fuzzy logical neurons capable of creating IF / THEN rules and use the extreme learning machine to update parameters. The proposed new model was submitted to linear and nonlinear regression databases to confirm that the proposed model can act as a universal approximation of functions. The results presented were promising about the ability to identify patterns within bases in complex problems.
Cutting Through the Emissions: Feature Selection from Electromagnetic Side-Channel Data for Activity Detection

Electromagnetic side-channel analysis (EM-SCA) has been used as a window to eavesdrop on computing devices for information security purposes. It has recently been proposed to use as a digital evidence acquisition method in forensic investigation scenarios as well. The massive amount of data produced by EM signal acquisition devices makes it difficult to process in real-time making on-site EM-SCA infeasible. Uncertainty surrounds the precise information leaking frequency channel demanding the acquisition of signals over a wide bandwidth. As a consequence, investigators are left with a large number of potential frequency channels to be inspected; with many not containing any useful information leakages. The identification of a small subset of frequency channels that leak a sufficient amount of information can significantly boost the performance enabling real-time analysis. This work presents a systematic methodology to identify information leaking frequency channels from high dimensional EM data with the help of multiple filtering techniques and machine learning algorithms. The evaluations show that it is possible to narrow down the number of frequency channels from over 20,000 to less than a hundred (81 channels). The experiments presented show an accuracy of 0.9315 when all the 20,000 channels are used, an accuracy of 0.9395 with the highest 500 channels after calculating the variance between the average value of each class, and an accuracy of 0.9047 when the best 81 channels according to Recursive Feature Elimination are considered.
Misinformation Containment in OSNs leveraging Community Structure

With the emergence of Online Social Networks (OSNs) as a major platform of communication, its abuse to spread misinformation has become a major threat to our society. In this paper, we study the misinformation containment problem in OSN. Given a snapshot of the OSN with a set of misinformed nodes, and a budget in terms of maximum number of seed nodes, the problem is to select the seed nodes, referred here as the beacon nodes, to plant the correct information, to minimize and eventually eradicate the misinformation at the earliest. We leverage the community structure of the OSN to select the beacon nodes, prioritizing the Community Boundary Nodes. To the best of our knowledge, this is the first work to exploit the topology of the OSN to combat misinformation spread. A modified form of Independent Cascade Model is followed to study the adversarial propagation of both misinformation and the correct information. Simulation on real data set shows that the proposed algorithm outperforms earlier algorithm [1] significantly in terms of maximum (average) infected time and the point of decline.