Reviewerly/exHarmony huggingface dataset:
Bridging the Gap Between Semantics and Control for Industry 4.0 and Autonomous Production

Small lot size production requires decoupled specification of the product and the production system to allow for flexible manufacturing, which leads to autonomous systems. Their key are algorithms, that are able to sequence and parametrize skills in a meaningful way such that a given task is fulfilled. This planning process requires suitable models of all relevant aspects of the production which includes the system's state and estimates of its behavior. In this paper we propose formal models for declarative and procedural knowledge, which unify the symbolic and subsymbolic representations. Currently, the curse of dimensionality prevents state of the art techniques to solve non-trivial real-world tasks that pose mixed discrete and continuous states. Our models are designed hierarchically from the very beginning and thus allow an automatic decomposition of planning problems. This allows us to compute solutions online for complex tasks that are only defined by the goal configuration of the product.We validate our approach with an assembly use-case that illustrates end-to-end autonomous robotic production in a realworld setting. The separate definition of product and hardware, as well as the number of steps required to fulfill this task demands a multilayered planning and execution for online control.
Abstraction and Subsumption in Modular Verification of C Programs

Representation predicates enable data abstraction in separation logic, but when the same concrete implementation may need to be abstracted in different ways, one needs a notion of subsumption. We demonstrate function-specification subtyping, analogous to subtyping, with a subsumption rule: if $$\phi $$ is a of $$\psi $$, that is $$\phi <:\psi $$, then $$x:\phi $$ implies $$x:\psi $$, meaning that any function satisfying specification $$\phi $$ can be used wherever a function satisfying $$\psi $$ is demanded. We extend previous notions of Hoare-logic sub-specification, which already included parameter adaption, to include framing (necessary for separation logic) and impredicative bifunctors (necessary for higher-order functions, i.e. function pointers). We show intersection specifications, with the expected relation to subtyping. We show how this enables compositional modular verification of the functional correctness of C programs, in Coq, with foundational machine-checked proofs of soundness.
The Review of Target Tracking for UAV

The target tracking based on UAV can effectively accomplish all kinds of tasks with the aid of vision processing system. In this paper, we focus on the single-target tracking, where the first frame of the rectangular calibration of the target is captured and used for target tracking in the subsequent frames. First, the visual tracking algorithms are briefly introduced, and the UAV based visual tracking algorithms are divided into generative and discriminative categories. The advantages and disadvantages of the target tracking methods based on UAV is summarized. Furthermore, the application of target tracking algorithms based on UAV are discussed and the developmental prospects for target tracking based on UAV are also provided.
A Cost-Effective CNN Accelerator Design with Configurable PU on FPGA

Convolutional neural networks (CNNs) are rapidly expanding and being applied to a vast range of applications. Despite its popularity, deploying CNNs on a portable system is challenging due to enormous data volume, intensive computation, and frequent memory access. Hence, many approaches have been proposed to reduce the CNN model complexity, such as model pruning and quantization. However, it also brings new challenges. For example, existing designs usually adopted channel dimension tiling which requires regular channel number. After pruning, the channel number may become highly irregular which will incur heavy zero padding and large resource waste. As for quantization, simple aggressive bit reduction usually results in large accuracy drop. In order to address these challenges, in this work, firstly we propose to use row-based tiling in the kernel dimension to adapt to different kernel sizes and channel numbers and significantly reduce the zero padding. Moreover, we developed the configurable processing units (PUs) design which can be dynamically grouped or split to support the tiling flexibility and enable efficient hardware resource sharing. As for quantization, we considered the recently proposed Incremental Network Quantization (INQ) algorithm which uses low bit representation of weights in power of 2 format, and hence is able to represent the weights with minimum computing complexity since expensive multiplication can be transferred into cheap shift operation. We further propose an approximate shifter based processing element (PE) design as the fundamental building block of the PUs to facilitate the convolution computation. At last, a case study of RTL-level implementation of INQ quantized AlexNet is realized on a standalone FPGA, Stratix V. Compared with the state-of-art designs, our accelerator achieves 1.87x higher performance, which demonstrates the efficiency of the proposed design methods.
Conceptual Insights in Blockchain Technology

The global popularity of digital cryptocurrencies and research in a decentralized system have led to the foundation of blockchain, which is fundamentally a public digital ledger to share information in a trustworthy and secure way. The concept and applications of blockchain have now spread from cryptocurrencies to various other domains, including business process management, smart contracts, IoT, and so on. Cryptocurrency is a mechanism designed to work for the online secure payments system using cryptography. Cryptography maintains confidentiality, integrity, and authentication. Cryptocurrency has come as a novel way of making payments that keep all the transactions secure and safe, which avoids any type of intermediaries such as a bank. This chapter will shed light on the concept of blockchain technology, security, and its applications in various domains.
Generalized Reciprocal Collision Avoidance for Non-holonomic Robots

It is challenging to study generalized reciprocal collision avoidance for non-holonomic robots, especially when angular velocities are required to be tuned. This paper investigates the reciprocal collision avoidance problem for non-holonomic robotics. Two strategies to adjust the linear and the angular velocities of robots are respectively, presented where the collision-free navigation is characterized as an analytical set of the changing ratios of linear velocities. It is shown that reciprocal collision avoidance can be achieved without using any linear approximation as long as the changing ratios fall into such set. The adjustment of the linear velocity has a priority to that of orientation such that the navigation efficiency of robots is maintained. Simulation results for some typical cases are presented to verify the effectiveness of the proposed algorithm.
Signal Processing Architectures, Algorithms, and Human–Machine Interactions in Urban Applications

The current state of art techniques used for signal processing and modeling comprises of vast number of instances of the framework inclusive of dissimilar modalities fused together such as vision, speech, textual data, language that essentially improves comprehension, modeling, performance processes of human and computer interface gadget and frameworks which augments human–human communication. Thus the all-embracing idea of this chapter will be solicitation of signal processing and computational methods based on challenges arising within vast number of disciplinary aspects used in city computing. Moreover, this chapter will state the capacities of and precincts of the present tech and analyzes the technical difficulties which should be overwhelmed in order to advance user-friendly and effective multi-modal interactive platform used of urban solicitation.
Potential of Culture for Sustainable Urban Development

Urban development, implemented on the principle of a creative city, is one of the principles of the transformation of urban structures, which is based on a through evaluation of its potential. It is one of the principles of the development of cities that are responsible for their environment (Sustainable City). A creative city is based on the city's dignities and the communities in which it lives. It builds on the specificities and creative potential of institutions, schools, the social composition of the population, but also on the particularities of the urban structure, the traditions of the city and its cultural climate. Many cities draw on this exception and use it for their own benefit and for the benefit of their citizens (Saint Etienne, Copenhagen, Lisbon, Graz, Glasgow, Košice...). For the prosperity of the city to be based on the principle of the development of culture and the creative industry, its potential for this type of transformation need to be assessed. The key factors that make the success of a "creative city" conditional are specific economic, social and political conditions. Favourable economic factors include: a labour market with a rich offer of a wide range of job opportunities, with existing creative industries, incubators, the IT sector, and intensive tourism. Relevant social factors include open social climate, characteristic tolerance, living city atmosphere, rich social life, informal social activities. Appropriate policy factors include: Urban policy and public administration applying the creative form of city management and working with local authorities and citizens. The character of the urban structure is also significant: its image, the quality of the urban environment, a rich array of cultural facilities, education based on the promotion of creative thinking. Just like the tradition of active social and cultural life. The paper aims to evaluate the potential of selected locations in Slovakia – Bratislava, Komárno and Lučenec. These cities represent two poles of the settlement structure in terms of their hierarchy, size, economic strength, localization. They have a strong cultural background. Their potential for applying the "smart city" principles, with an emphasis on the use of cultural potential, is analysed and subsequently evaluated through a range of economic, political and social criteria that correspond to the principles of a cultural city. In view of the authors' focus on urban planning, the assessment of the potential of the urban structure of the selected cities is an essential consideration as one of the factors determining the development of their creative base. The goal is intensity and quality of the cultural activities, which supporting the transformation of the urban structure with a view to viable and sustainable urban development.
A Multitenant Data Store Using a Column Based NoSQL Database

As the world is frantically moving towards digitization, handling huge volumes of data is becoming complex and difficult to optimize. This is mainly because of increasing dimensions of data, its unstructuredness and increased need for storage space. To increase the efficiency of the cloud environment, many service providers have moved towards multi-tenant architecture. In a multi-tenant architecture, a number of individual applications termed as tenants work under a shared environment. In such a scenario, it is imperative that data of one tenant be isolated from that of other tenants. Further, it is also desirable to optimize the use of storage space. With these considerations, this paper proposes a multi-tenant database architecture for applications using a column-based NoSQL data store. The architecture has been implemented with the NoSQL database, Cassandra.
Analyzing Likert Scale Inter-annotator Disagreement

Assessment of annotation reliability is typically undertaken as a qualityVogel, Carl assurance measure in order to provide a sound fulcrum for establishingKoutsombogera, Maria the answers to research questions that requireCostello, Rachel the annotated data. We argue that the assessment of inter-rater reliability can provide a source of information more directly related to the background research. The discussion is anchored in the analysis of conversational dominance in the MULTISIMO corpus. Other research has explored factors in dialogue (e.g. big-five personality traits and conversational style of participants) as predictors of independently perceived dominance. Rather than assessing the contributions of experimental factors to perceived dominance as a unitary aggregated response variable following verification of an acceptable level of inter-rater reliability, we use the variability in inter-annotator agreement as a response variable. We argue the general applicability of this in exploring research hypotheses that focus on qualities assessed with multiple annotations.
Enhancing STEM Education using Augmented Reality and Machine Learning

Learning Science, Technology, Engineering and Mathematics (STEM) in the 21st century has been evolved from the conventional textbook to the interactive platform using electronic devices. This paper presents the implementation of a mobile application system, named AUREL (Augmented Reality Learning) in enhancing the learning experience by projecting Augmented Reality (AR) objects onto 2D images. This AR visualization is used to improve the understanding of STEM subjects and increases the enthusiasm of students towards STEM subjects. In this implementation, Google's Cloud Tensor Processing Units (TPUs) are used to train specific datasets alongside Cloud Vision API to detect a wide range of objects. ML Kit for Firebase is used to host the custom TensorFlow Lite models for specific use cases for better accuracy. On the other hand, Google Cloud Platform (GCP) is used to harvest STEM data, manage STEM 3D information and data processing. Subsequently, the processed information will be displayed in AR in the mobile application using ARCore's Sceneform SDK. The application of AUREL could be extended to all science subjects so that students can learn using an interactive platform.
Internet Fraud

Account takeover is a form of online identity theft where a fraudster gains unauthorized access to an individual's account in a given system. Depending on the system, this unauthorized access can lead to severe consequences of privacy breach and financial loss to the victims, to the companies that maintain the system and to other users. In this paper, we present the work done in order to prevent and detect account takeovers at mobile.de, an online vehicle marketplace. To tackle the prevention problem, we first present a behavioral analysis of how fraudsters operate, and implemented a mutual two-factor authentication that achieved a reduction of 43% of account takeovers. To tackle the detection problem, we introduce a concept drift sensitive machine learning training approach that was able to improve our baseline methods by 18% in detection rates. The automatic detection reduced the exposure of fraudulent listings by 69%, resulting in a safer marketplace for buyers and sellers.
Deep learning for software-based turbulence mitigation in long-range imaging

Optical imaging over long horizontal paths often suffers from the effects of atmospheric turbulence. Dynamic density variations in the air result in random spatiotemporally varying shifts and blurs in the recorded images. Software based turbulence mitigation algorithms provide a means of computationally reducing these turbulence effects in video. They provide camera operators with sharper and more stable imagery which supports them on visual recognition and identification tasks. Most turbulence mitigation algorithms rely on a form of temporal low pass filtering to remove the turbulence induced fluctuations. This filtering also suppresses high spatial frequency information and thus limits the ability of these algorithms to recover fine details. Here we propose a turbulence mitigation algorithm which employs a deep neural network to recover high spatial frequency information from turbulence degraded video. The proposed algorithm builds on our previous approach, where frame-to-frame estimates of image shifts are used to combine multiple frames. This approach is amended by using a deep neural network to deblur output images. For the related task of single image super-resolution, we have previously shown that such neural networks provide state-of-the-art image reconstruction performance. Here our neural network is trained using semi-synthetic image sequences of static scenes with simulated turbulence. We show that our deep learning based approach provides on semi-synthetic test data a substantial performance increase compared to our previous sharpening approach. We also apply our algorithm to real long range imagery of ships at sea and find a perceptually similar improvement in image quality as for semi-synthetic data.
A Tool to Study Fractals in an Interdisciplinary Perspective

Fractals are geometric objects used to describe irregular shapes that have a fractional dimension and commonly appear in nature. Although several proposals for the study of perfect fractals at the basic level are present in the literature, only few proposals for the study of real fractals exist, which does not seem reasonable considering the wealth of the theme and its potentiality as an interdisciplinary topic. In this text, we present a simple and easily assimilable procedure to calculate the fractal dimension of any two-dimensional object. It only demands a computer with the free software ImageJ installed. This procedure is easily understandable, considering the basic school curriculum, and easily usable in a variety of school settings.
Rainfall Forecasting using the Classification and Regression Tree (CART) Algorithm and Adaptive Synthetic Sampling (Study Case: Bandung Regency)

Indonesia is a country that can experience potentially adverse climate change. More than 50% of the population in Bandung Regency works in the agricultural sector. Hence, the prediction of rainfall is essential in agriculture to produce the best harvest and to minimize losses. In this study, a Classification and Regression Tree (CART) algorithm were used to forecast the rainfall in Bandung Regency. Furthermore, an Adaptive Synthetic Sampling (ADASYN) algorithm was added to optimize the model produced due to a class imbalance in the data. The weather data was collected from the Meteorology, Climatology and Geophysics Agency of Indonesia (BMKG) from 2005-2017. The results showed that using the CART algorithm yielded 93.94% rainfall prediction accuracy with a 1.38 s running time whereas using ADASYN and CART yielded an accuracy of 98.18% with a 1.48 s running time.
Systems Evaluation for Access Management of Multiscale Transportation Networks

In transportation networks, access management is the systematic approach to control the location, spacing, design, and operation of residential and commercial access points, where a main goal is to preserve the safety and capacity of the system. The literature describes how access management programs can help in saving billions of dollars by reducing the number of crashes and improving road capacity. However, it is common for transportation agencies to have limited resources, including budgets, equipment, time, human resources, and others, and thus they need a framework to prioritize their access management needs among hundreds of thousands of access points. While current prioritization frameworks in the transportation literature have several limitations, this research develops a prioritization framework that combines data-driven quantitative analysis with modeling and expertise of transportation professionals to prioritize access management projects that focus on safety improvement. This will be accomplished by identifying a set of site-specific (quantitative and qualitative) factors that represent engineering characteristics of locations where access points are located and have an impact on the safety of road users. Next, these factors are used to develop a classification criterion to classify each access point into two categories (in terms of safety): critical and noncritical. The results of this analysis are then demonstrated spatially using innovative straight-line diagrams, where the main goal is to allow decision makers to easily identify and prioritize anomalous access points. To demonstrate the developed framework, it was applied to thousands of access points located on four major U.S. highway. The results of this demonstration should assist decision makers to prioritize these access points based on a safety improvement perspective.
On the Taut String Interpretation and Other Properties of the Rudin–Osher–Fatemi Model in One Dimension

Abstract We study the one-dimensional version of the Rudin–Osher–Fatemi (ROF) denoising model and some related TV-minimization problems. A new proof of the equivalence between the ROF model and the so-called taut string algorithm is presented, and a fundamental estimate on the denoised signal in terms of the corrupted signal is derived. Based on duality and the projection theorem in Hilbert space, the proof of the taut string interpretation is strictly elementary with the existence and uniqueness of solutions (in the continuous setting) to both models following as by-products. The standard convergence properties of the denoised signal, as the regularizing parameter tends to zero, are recalled and efficient proofs provided. The taut string interpretation plays an essential role in the proof of the fundamental estimate. This estimate implies, among other things, the strong convergence (in the space of functions of bounded variation) of the denoised signal to the corrupted signal as the regularization parameter vanishes. It can also be used to prove semi-group properties of the denoising model. Finally, it is indicated how the methods developed can be applied to related problems such as the fused lasso model, isotonic regression and signal restoration with higher-order total variation regularization.
Static detection of real-world buffer overflow induced by loop

Buffer overflow (BoF) is one of the most dangerous security vulnerabilities. A BoF can be induced by functions, such as the memcpy family, or loops with pointer or array operations. Static detection of BoF is a well-defined method and often performed before system deployment. However, most of previous static techniques either detect the BoFs induced by functions or analyze simple loops that induce BoFs, such as the single loop with a single loop variable. In order to clearly understand the impact of loops on BoF, we perform an empirical study on real-world BoFs detected in the past three years, including 211 BoFs from 60 open-source systems, and find that about a third of these BoFs are induced by loops. Motivated by this observation, we propose a novel static detection technique, DBloop, to localize BoFs induced by loops. The key of DBloop is to get the extremum length of data-movement on the buffer by analyzing target loops and then check whether the buffer overruns using constraint solving. We have implemented DBloop with CIL, and evaluated it on real-world programs detection by comparing with a commercial tool, Checkmarx, and an open-source tool, Splint. DBloop successfully localizes 57 BoFs induced by loops, while Checkmarx and Splint only detect 4 and 3, respectively. Moreover, DBloop has detected 4 new BoFs that have not been reported before.