Reviewerly/exHarmony huggingface dataset:
MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching

Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems. State-of-the-art stereo matching methods based on convolutional neural networks require intensive computations on GPUs and are difficult to deploy on embedded systems. In this paper, we propose MTStereo2.0, an improved version of the MTStereo stereo matching method, which includes a more robust context-driven cost function, better detection of incorrect matches and the computation of disparity at pixel level. MTStereo provides accurate sparse and semi-dense depth estimation and does not require intensive GPU computations. We tested it on several benchmark data sets, namely KITTI 2015, Driving, FlyingThings3D, Middlebury 2014, Monkaa and the TrimBot2020 garden data sets, and achieved competitive accuracy. The code is available at https://github.com/rbrandt1/MaxTreeS.
Refactoring the Whitby Intelligent Tutoring System for Clean Architecture

Abstract Whitby is the server-side of an Intelligent Tutoring System application for learning System-Theoretic Process Analysis (STPA), a methodology used to ensure the safety of anything that can be represented with a systems model. The underlying logic driving the reasoning behind Whitby is Situation Calculus, which is a many-sorted logic with situation, action, and object sorts. The Situation Calculus is applied to Ontology Authoring and Contingent Scaffolding: the primary activities within Whitby. Thus many fluents and actions are aggregated in Whitby from these two sub-applications and from Whitby itself, but all are available through a common situation query interface that does not depend upon any of the fluents or actions. Each STPA project in Whitby is a single situation term, which is queried for fluents that include the ontology, and to determine what pedagogical interventions to offer. Initially Whitby was written in Prolog using a module system. In the interest of a cleaner architecture and implementation with improved code reuse and extensibility, the initial application was refactored into Logtalk. This refactoring includes decoupling the Situation Calculus reasoner, Ontology Authoring framework, and Contingent Scaffolding framework into third-party libraries that can be reused in other applications. This extraction was achieved by inverting dependencies via Logtalk protocols and categories , which are reusable interfaces and components that provide functionally cohesive sets of predicate declarations and predicate definitions . In this paper the architectures of two iterations of Whitby are evaluated with respect to the motivations behind the refactor: clean architecture enabling code reuse and extensibility.
Development, Analysis, and Real-life Benchmarking of RRT-based Path Planning Algorithms for Automated Valet Parking

One of the major application areas of highly automated vehicles is the problem of Automated Valet Parking (AVP). In this work, we analyze solutions and compare performances of RRT (rapidly exploring random tree) based approaches in the context of the AVP problem, which can also be applied in a more general low-speed autonomy context. We present comparison results using both simulation and real-life experiments on a representative parking use case. The results indicate better suitability of RRTx and RRV for utilization in typical AVP scenarios. The main contributions of this work lie in real-life experimental validation and comparisons of RRT approaches for use in low-speed autonomy.
Web-based Buleleng regency agriculture product information system development

Abstract Agriculture is very important for the welfare of the nation. However, accurate and up-to-date agriculture data in the Buleleng regency are difficult to find. This study was aimed at making a developmental design of Web-based Buleleng regency agriculture product information system and implementing it to support the region’s best products-based regional people economy. The development of this system used the Software Development Life Cycle (SDLC) method with Waterfall model that followed five stages of activities, namely (a) requirements definition, (b) system, and software design, (c) implementation and unit testing, (d) integration and testing, and (e) operation and maintenance. The data needed in the system was obtained by working together with the Buleleng regency agricultural office. Later this system will involve some users with different accessing rights, namely field agriculture extension agents who manage product data, the workers of the agriculture office as administrators of the system, the general public, and consumers who probably come from hotels, restaurants, supermarkets, etc that become the markets of the agriculture products in Buleleng regency. Consumers can order the products. However, this system does not handle online payments. It is expected that with this system Buleleng regency will have accurate and up-to-date data related to agriculture products that are available in Buleleng. In addition, this system can be used as a model for other regencies in Bali and other regions in Indonesia. With the availability of this system in the future, the farmers as producers will get good prices for their products and the consumers will get products of good quality at reasonable prices.
HaPPY-Mine: Designing a Mining Reward Function

In cryptocurrencies, the block reward is meant to serve as the incentive mechanism for miners to commit resources to create blocks and in effect secure the system. Existing systems primarily divide the reward in proportion to expended resources and follow one of two static models for total block reward: (i) a fixed reward for each block (e.g., Ethereum), or (ii) one where the block reward halves every set number of blocks (e.g., the Bitcoin model of halving roughly every 4 years) but otherwise remains fixed between halvings. In recent work, a game-theoretic analysis of the static model under asymmetric miner costs showed that an equilibrium always exists and is unique [4]. Their analysis also reveals how asymmetric costs can lead to large-scale centralization in blockchain mining, a phenomenon that has been observed in Bitcoin and Ethereum and highlighted by other studies including [11, 16]. In this work we introduce a novel family of mining reward functions, HaPPY-Mine (HAsh-Pegged Proportional Yield), which peg the value of the reward to the hashrate of the system, decreasing the reward as the hashrate increases. HaPPY-Mine distributes rewards in proportion to expended hashrate and inherits the safety properties of the generalized proportional reward function established in [9]. We study HaPPY-Mine under a heterogeneous miner cost model and show that an equilibrium always exists with a unique set of miner participants and a unique total hashrate. Significantly, we prove that a HaPPY-Mine equilibrium is more decentralized than the static model equilibrium under a set of metrics including number of mining participants and hashrate distribution. Finally, we show that any HaPPY-Mine equilibrium is also safe against collusion and sybil attacks, and explore how the market value of the currency affects the equilibrium.
Ket Quantum Programming

Quantum programming languages (QPL) fill the gap between quantum mechanics and classical programming constructions, simplifying the development of quantum applications. However, most QPL addresses the inherent quantum programming problem, neglecting quantum computer implementation constraints. We present a runtime architecture for classical-quantum execution that mitigates the limitation of interaction between classical and quantum computers originated from the cloud-based model of quantum computation provided by several vendors, which implies a quantum computer processing in batch. In the proposed runtime architecture, we introduce (i) runtime quantum code generation to enable generic quantum programming and dynamic quantum execution; and (ii) the concept of futures to handle dynamic interaction between classical and quantum computers. To support our proposal, we have implemented the Ket Quantum Programming framework that features a Python-embedded classical-quantum programming language named Ket, the C++ quantum programming library Libket, and Ket Bitwise (quantum computing) Simulator. The last one improves over the bitwise representation, making the simulation time not dependent on the number of qubits but the amount of superposition and entanglement of simulation.
MEMA-NAS: Memory-Efficient Multi-Agent Neural Architecture Search

Object detection is a core computer vision task that aims to localize and classify categories for various objects in an image. With the development of convolutional neural networks, deep learning methods have been widely used in the object detection task, achieving promising performance compared to traditional methods. However, designing a well-performing detection network is inefficient. It consumes too much hardware resources and time to trial, and it also heavily relies on expert knowledge. To efficiently design the neural network architecture, there has been a growing interest in automatically designing neural network architecture by Neural Architecture Search (NAS). In this paper, we propose a Memory-Efficient Multi-Agent Neural Architecture Search (MEMA-NAS) framework in end-to-end object detection neural network. Specifically, we introduce the multi-agent learning to search holistic architecture of the detection network. In this way, a lot of GPU memory is saved, allowing us to search each module’s architecture of the detection network simultaneously. To find a better tradeoff between the precision and computational costs, we add the resource constraint in our method. Search experiments on multiple datasets show that MEMA-NAS achieves state-of-the-art results in search efficiency and precision.
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming

In tabular multi-agent reinforcement learning with average-cost criterion, a team of agents sequentially interacts with the environment and observes local incentives. We focus on the case that the global reward is a sum of local rewards, the joint policy factorizes into agents’ marginals, and full state observability. To date, few global optimality guarantees exist even for this simple setting, as most results yield convergence to stationarity for parameterized policies in large/possibly continuous spaces. To solidify the foundations of MARL, we build upon linear programming (LP) reformulations, for which stochastic primal-dual methods yield a model-free approach to achieve optimal sample complexity in the centralized case. We develop multi-agent extensions, whereby agents solve their local saddle point problems and then perform local weighted averaging. We establish that the sample complexity to obtain near-globally optimal solutions matches tight dependencies on the cardinality of the state and action spaces, and exhibits classical scalings with respect to the network in accordance with multi-agent optimization. Experiments corroborate these results in practice.
Importance of Training Sample Based on SRGAN

Generative Adversarial Network has high capabilities for generating realistic images and other real-life applications due to its generator-discriminator interactions. Like other neural networks in deep learning, an efficient training process is indispensable to model performance. With this feature, many prior works that aimed to optimize the learning process within this structure focused on importance sampling and training datasets. Aside from finding important data and accelerating the learning process, these studies have a common assumption: gradient descent can directly indicate data importance. However, for image data in high dimensional space, gradient descent could potentially be misleading. Hence, this study offers a different insight into data importance: we want to verify if using a gradient descent path is a reliable method for indicating data importance. Furthermore, if such important data exists, we also want to know if the importance persists in multiple training processes (with different input orders). After conducting five experiments with each trial rearranging the input order of the training set on the SRGAN model, we found that data importance cannot be indicated by any individual metric such as gradient, and the order has a great impact on the gradient descent path.
(CSM2-RA-R2-TI): Cyber Security Maturity Model for Risk Assessment Using Risk Register for Threat Intelligence

Abstract Because of the growing frequency sophistication, and severity of cyber security attacks, all businesses should ensure that cyber security risk is properly addressed in their enterprise risk management (ERM) programs. Risk Management, agreeing with the NIST Guide 8286 is the arrangement of “composed exercises to direct and control an association concerning risk”. NISTIR 8286 characterizes a system and framework for hazard the executives. In any case, executing this norm without an in-depth plan can turn into a risk on associations. This paper provides a capability maturity model for risk assessment for threat intelligence using a risk register. This model helps the organization as reference and set clear path to survey risk assessments in accordance with latest threats.
Implementation of Optimized VLSI Architecture for Montgomery Multiplication Algorithm

Montgomery multiplication algorithm is widely used in many cryptographic algorithms to provide secure communication between the transmitter and receiver. The main objective of this paper is to study the potential gains of Montgomery algorithm compared to classic ones, For the speedy operation, Montgomery modular multiplication algorithm employs carry–save addition to reduce the carry propagation time at each cycle of addition, but it will increase the hardware complexity. Using carry–save adder as standard, carry–select, carry–skip adders are considered with different optimized hardware versions and thus increases the performance. Simulation is done by using Xilinx design 14.2. The pro-posed Montgomery algorithm design is then compared with the classic one in terms of power, area, delay and thus can achieve improved performance and speed when compared to the classic Montgomery multiplier.
Adding Emotions to Models in a Viewpoint Modelling Framework From Agent-Oriented Software Engineering

In modern software development, considering the viewpoints of stakeholders is an important step in building the right system. Over the past decade, several authors have proposed solutions to capture and model these viewpoints. While these solutions have been successful, emotions of stakeholders have been largely ignored. Considering the emotional needs of stakeholders is important because both the users' perceptions of a product and their use of a product are influenced by emotion as much as cognition. Building on recent work in modelling the emotional goals of stakeholders, the authors extend an existing viewpoint framework to capture emotions, and to use emotions in models from early-phase requirements to detailed software design. They demonstrate the models and framework with a case study of an emergency alarm system for older people, presenting a complete set of models for the case study. The authors introduce recent experience in using emotional models in requirements elicitation within an agile process.
Analysis of entry behavior of students on job boards in Japan based on factorization machine considering the interaction among features

Job-hunting activities in Japan are different from those in other countries. The features of this are the simultaneous recruitment of new graduates, joining the company in April, and the use by most students of such resources as employment information websites. In recent years, website job boards for new graduates have provided Japanese students with assistance in finding companies for which they want to work. On these boards, students can bookmark companies that they are interested in before deciding to apply. After bookmarking, a company bookmarked by a user can examine the information again later. However, even if the students rate various companies, many of these bookmarks do not lead to job applications. In other words, this can be regarded as a lost opportunity for gaining job applications from the perspective of the company. It is important for companies to gain as many job applications as possible to be successful in their recruitment activities. Therefore, a method of analyzing the entry behavior of students on job boards using factorization machines is proposed. The model predicts whether a student will submit a job application to a company. The prediction is based on student attributes and activity information, as well as information about the companies that they are interested in, as input variables. The interactions between input variables are also considered in making the prediction. In addition, the method supports student job-hunting activities and company measures for targeting students. To clarify the proposed model, analytical experiments were conducted with actual data from a website job board for new graduates.
Remote Real Time Monitoring System of Slope and Its Application in Sustainable Mining of Open Pit Coal Mine

Abstract The mining of open-pit coal resources will form a large number of mine slopes and the geological hazard of slope landslide restricts the sustainable production of open-pit coal mine, monitoring and early warning of landslide disasters can reduce the losses caused by landslide disasters but now the most widely used displacement monitoring technology can not achieve the function. Based on the mechanism of landslide mechanics, the new remote real time monitoring method is proposed and its equipment system is developed, the remote real-time monitoring and early warning work of Antaibao open-pit coal mine slope is carried out. The monitoring locations are determined by finite difference numerical simulation method, through the remote monitoring results and slope stability analysis indicate that the slope is at a stable state. It has been proved by engineering practice that the monitoring of slope mechanics can ensure security of the continuous mining of the mine.
Memory Bank Clustering for Self-supervised Contrastive Learning

Contrastive Learning aims at embedding positive samples close to each other and push away features from negative samples. This paper analyzed different contrastive learning architectures based on the memory bank network. The existing memory-bank-based model can only store global features across few data batches due to the limited memory bank size, and updating these features can cause the feature drift problem. After analyzing these issues above, a network for contrastive learning with visual representations is proposed in this paper. First, the model is combined with a memory bank and memory feature clustering mechanism; Second, a new feature clustering method is proposed for memory bank network to find and store cross-epoch global feature centers for training epochs based on the memory bank architecture. Third, the centers in memory bank are treated as class features to construct positive and negative samples with current batch data and apply contrastive learning methods to optimize a feature encoder to learn a better feature representation. Finally, this paper designed a training pipeline to update the memory bank and encoder individually to circumvent the feature drift problem. To test the performance of proposed memory bank clustering method with on unsupervised image classification, our experiment used a self-supervised online evaluator with an extra non-linear layer. The experiment results show that our proposed model can achieve good performance on image classification tasks.
Deep Learning Based Human to Human Interaction Detection Using Wireless Fidelity

Recognizing activity specifically humans, plays a vital role in security alarm systems, elder, fall detection, gesture recognition, smart home systems, and many other fields. Many researchers are being carried out for device-free activity recognition, gesture recognition, elder fall detection, etc. With the increase in technological advancement, the deployment of Wi-Fi has increased. With the presence of Wi-Fi signals almost everywhere in the environment, it helps to detect human activities by the virtue of its property. The Channel State Information (CSI) of the Wi-Fi signal collected contains both amplitude and phase information of Wi-Fi signals. The channel state estimation provides vital information about the channel properties of a communication link which helps in Human activity recognition, as well as human to human interactions. We leverage the change of amplitude in the Wi-Fi signals when there is a human activity or human-to-human interaction. The paper uses the deep learning method which is used to classify five human-to-human interactions in three different phases. The proposed convolutional neural network can identify 5 classes (approaching, departing, high-five, handshake, and hug) of Human-Human Interactions (HHI) with an accuracy of 95.76%.
Exploring the Freedoms in Data Mining: Why the Trustworthiness and Integrity of the Findings are the Casualties, and How to Resolve These?

This work aims to improve the accuracy of data-mining. A problem with today’s system for Artificial Intelligence (AI) is the reliance on frameworks that lacks oversight: AI is trained on data that is not guaranteed to be representative and is constructed through software-APIs maintained by a large number of developers, each with their interests. In contrast, the society expects the systems to work, as the result is otherwise a zombie-like behavior of systems for industrial control of how new drugs are discovered, oversight of public governance, heating systems, etc. This paper aims at tackling this issue. The idea is to relate the freedoms in software and algorithms, thereby identifying the blind spots in AI. The results reveal how knowledge discovery is directly linked to hidden attributes (in software and algorithms). Thus, this work provides users with a recipe for improving the trust in their AI-predictions.
Interaktions-ergonomische Gestaltung und Kompatibilität von AR-unterstützten Informationsdarstellungen am Beispiel eines Head-Mounted Displays für industrielle Rüstvorgänge

In the course of technological progress, analog solutions are increasingly replaced by digital ones. In this context, novel visualization options, such as Augmented Reality (AR), are more and more important. However, handling AR-based information displays can lead to performance losses and increased physical as well as psychological strain if the design is incompatible. Due to rapid technological advancements, existing ergonomic and user-centered guidelines and legal regulations can only support the design and usability of such systems to a limited extent. This paper presents a practical example by showing to what extent a cyber-physical set-up application in the form of a head-mounted display complies with or meets the applicable interaction ergonomics and compatibility standards in the context of industrial activities and gesture-controlled, binocular AR systems.Practical Relevance: The results presented play an important role in practice, since the system presented is intended to support workers in complex set-up and assembly processes. Based on these findings and with the help of further optimization possibilities the aim is to achieve a process-safe, value-adding and strain-minimizing use of AR systems in industrial environment.