Reviewerly/exHarmony huggingface dataset:
Laser-based Collision Avoidance and Reactive Navigation using RRT* and Signed Distance Field for Multirotor UAVs

Collision avoidance plays a crucial role for autonomous navigation in unknown dynamic environments and still remains an ongoing research problem. In this paper, we present a new collision avoidance algorithm by combining an RRT* path planner with a Signed Distance Field (SDF) based collision checking algorithm, in which the trajectory is optimized by a short cut and Optimal Polynomial Trajectory algorithms. The proposed algorithm is integrated to work in combination with a Model Predictive Control (MPC) based trajectory controller in order to provide a complete system for reactive navigation purposes. A thorough evaluation of the proposed algorithm has been conducted in several simulating scenarios using RotorS Gazebo simulator, showing fast collision checking capabilities in the presence of static and dynamic obstacles. The results show that the proposed algorithm outperforms in 76.93% considering the processing time when tested in a 1000 × 1000 pixels map. The results also demonstrate that the proposed navigation algorithm allows the safe navigation of a multirotor Unmanned Aerial Vehicle (UAV).
Implementation of a Depth from Light Field Algorithm on FPGA

A light field is a four-dimensional function that grabs the intensity of light rays traversing an empty space at each point. The light field can be captured using devices designed specifically for this purpose and it allows one to extract depth information about the scene. Most light-field algorithms require a huge amount of processing power. Fortunately, in recent years, parallel hardware has evolved and enables such volumes of data to be processed. Field programmable gate arrays are one such option. In this paper, we propose two hardware designs that share a common construction block to compute a disparity map from light-field data. The first design employs serial data input into the hardware, while the second employs view parallel input. These designs focus on performing calculations during data read-in and producing results only a few clock cycles after read-in. Several experiments were conducted. First, the influence of using fixed-point arithmetic on accuracy was tested using synthetic light-field data. Also tests on actual light field data were performed. The performance was compared to that of a CPU, as well as an embedded processor. Our designs showed similar performance to the former and outperformed the latter. For further comparison, we also discuss the performance difference between our designs and other designs described in the literature.
Humanoid Robots: Use Cases as AI-Lab Companion : Can an empathic and collaborative digital companion motivate innovation?

This paper describes several use cases for a humanoid robot in an innovation lab environment. They are implemented on Pepper, a commercially available robot. Pepper is used as digital consultant in the Siemens AI-Lab. The different functionalities range from being a concierge for new guests to being a tutor for AI or do answering questions in open and project-specific domains. Besides the built-in speech and visual recognition system provided by the robot our own developed AI technologies are integrated.
Automated Cyber Threat Sensing and Responding

Cyber security management requires fast and cost efficient responses to threat alerts. Automation of cyber threat sensing and responding is one way to achieve immediate reactions to imminent threats. There are already tools for an extensive automation of threat sensing, e.g. threat intelligence sharing platforms. Methods, techniques and tools for reacting to menacing states and events, e.g. security-policy-controlled systems, have also been explored and published for some time. What is still missing, however, is the integration of these two approaches. This paper describes first steps towards an integration of threat intelligence sharing platforms and security-policy-controlled systems. We present a conceptual design for threat reaction strategies, security architectures and mechanisms and information representation requirements. We use two exemplary threat scenarios to demonstrate our proposals.
A Latency-Optimized Hash-Based Digital Signature Accelerator for the Tactile Internet

Tactile Internet as evolution of the Internet of Things will enable real-time interactive applications in industry and society. It requires low latency and security. Security comprises encryption and data authentication. Digital signatures enable the latter. With the rise of quantum computers, most currently employed digital signature schemes will become unsecure. One promising post-quantum secure algorithm is the eXtended Merkle Signature Scheme (XMSS). It is computationally expensive and thus contradicts low latency requirements. This paper proposes a latency-optimized accelerator for hash-based digital signature processing for the XMSS algorithm. Our architecture improves the latency of signing and verification into the sub-millisecond range.
Integrated Mapping and Path Planning for Very Large-Scale Robotic (VLSR) Systems

This paper develops a decentralized approach for mapping and information-driven path planning for Very Large Scale Robotic (VLSR) systems. In this approach, obstacle mapping is performed using a continuous probabilistic representation known as a Hilbert map, which formulates the mapping problem as a binary classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map fusion method is presented that quickly and efficiently combines the information from individual robot maps. An integrated mapping and path planning algorithm is presented to determine paths of maximum information value, while simultaneously performing obstacle avoidance. Furthermore, the effect of how percentage communication failure effects the overall performance of the system is investigated. The approach is demonstrated on a VLSR system with hundreds of robots that must map obstacles collaboratively over a large region of interest using onboard range sensors and no prior obstacle information. The results show that, through fusion and decentralized processing, the entropy of the map decreases over time and robot paths remain collision-free.
Information leakage of multi-threaded programs

Quantitative information flow is an important technique for measuring information leakage of a program. It is widely used in analyzing anonymity protocols and timing channels. One area of interest is to quantify the information leakage of multi-threaded programs, which has not been well-studied in prior work. In this paper, an automated trace-based approach is proposed to precisely quantify information leakage of shared-memory multi-threaded programs. The approach takes into account the effect of schedulers and leakage in intermediate states of executions. The programs are modeled by Markovian processes. Then, variants of information leakage, including expected, bounded time, maximum, and minimum leakages are measured. The validity of the approach is demonstrated by implementing it in a tool PRISM-Leak, which is built upon PRISM, a probabilistic model checker. Finally, two case studies are utilized to analyze and compare the approach against state-of-the-art leakage quantification tools.
iBaby

This paper designs and implements a mobile children monitoring and finding system, called iBaby, using wearable devices and nearby smartphones to detect unexpected holding and find missing children through Internet of Things (IoT) technologies, respectively. In the monitoring mode, the iBaby system can prevent young children from taking away by strangers/people with bad intentions. In the finding mode, the iBaby system can cooperatively find missing children equipped with hand wearable devices consisting of the mobile iBeacon and 3-axis accelermeter through crowdsourced sensing networks formed by smartphone users with outdoor GPS and indoor IoT localization. To accurately detect stranger holding behaviors, multi-feature based, artificial neural network based, and convolutional neural network based posture recognition methods are designed to improve recognition success rates of iBaby as much as possible. In particular, an iOS-based prototype with Arduino wearable devices and static iBeacon nodes is implemented to verify the feasibility and correctness of our iBaby system.
Key-Track: A Lightweight Scalable LSTM-based Pedestrian Tracker for Surveillance Systems

There has been a growing interest in leveraging state of the art deep learning techniques for tracking objects in recent years. Most of this work focuses on using redundant appearance models for predicting object tracklets for the next frame. Moreover, not much work has been done to explore the sequence learning properties of Long Short Term Memory (LSTM) Neural Networks for object tracking in video sequences. In this work we propose a novel LSTM tracker, Key-Track, which effectively learns the spatial and temporal behavior of pedestrians after analyzing movement patterns of human key-points provided to it by OpenPose [3]. We train Key-Track on single person sequences that we curated from the Duke Multi-target Multi-Camera (Duke-MTMC) [26] dataset and scale it to track multiple people at run-time, further testing its scalability. We report our results on the Duke-MTMC dataset for different time-series sequence lengths we feed to Key-Track and find three as the optimum time-step sequence length producing the highest Average Overlap Score (AOS). We further present our qualitative analysis on these different time-series sequence lengths producing different results depending on the type of video sequence. The total observed size of Key-Track is under 1 megabytes which paves its way into mobile devices for the purpose of tracking in real-time.
Quaternion-Based Backtracking Search Optimization Algorithm

Fitness landscape has been one of the main limitations regarding optimization tasks. Although meta-heuristic techniques have achieved outstanding results over a large variety of problems, some issues related to the function geometry and the risk to get trapped from local optima are issues that still require attention. To deal with this problem, we propose the Quaternion-based Backtracking Search Optimization Algorithm, a variant of the standard Backtracking Search Optimization Algorithm that maps each decision variable in a tensor onto a hypercomplex search space, whose landscape is expected to be smoother. Experiments conducted using nine benchmarking functions showed considerably better results than the ones achieved over standard search spaces, as well as more accurate results than some quaternion-based methods as well.
On Misuse of Nonce-Misuse Resistance : Adapting Differential Fault Attacks on (few) CAESAR Winners

New cryptographic schemes are often built upon old and proven primitives which withstood long public scrutiny. The recently concluded CAESAR competition saw several authenticated ciphers which were directly built upon proven primitives like AES. However, any attacks associated with these underlying primitives become a vulnerability to the whole scheme. AES, which is considered theoretically secure, has a very low fault resistance against differential fault attacks (DFA) requiring only 1-2 faults. In this paper, we study DFA attacks on some of the CAESAR competition winners with AES block cipher as the underlying primitive. We study the challenges imposed by the design of these modes, such as masking of the cipher-text. We also show that a very small number of nonce repetition and faults is required to extend the original attack on AES, which makes it very practical. We show that OCB and COLM need 1 nonce repetition and 3 faults only to uniquely identify the Key.
How language works &amp; what machines can do about it

The current skills shortage in dialog system development is being filled by very clever graduates that are however victims of historical forces. The current batch of engineers, at all levels, need to study history rather than repeat it. The claim is that techniques from the human and social sciences might provide the way forward.
Institutionalizing and Sustaining Virtual Reality Experiences

The speakers on this panel will discuss the curricular uses, partnership opportunities, infrastructure requirements, and planning results of institutionalizing for the sustained support of Virtual Reality experiences for teaching and learning in library settings. The panel will provide an opportunity for those with some familiarity of emerging technologies to extend their understanding of the field, and help develop ideas for new research and teaching areas related to information science and digital librarianship. The panel will also motivate JCDL audience participation and discussion for potential Virtual Reality development uses in research settings beyond academic libraries. Supporting Virtual Reality research, pedagogy, and content creation requires extensive cross-campus collaboration, and the session will highlight the conference theme of connecting people to achieve extraordinary results.
Challenges in the Path Toward a Scalable Silicon Photonics Implementation of Deep Neural Networks

The wide utility provided by the computational algorithms known as “deep learning” (or artificial neural networks) has prompted the development of specialized (electronic) hardware that can efficiently meet the speed and energy requirements that they demand. In the last few years, several proposals have also been made to build integrated photonic platforms to implement these algorithms by harnessing the light's ability to efficiently perform several kinds of computational operations with low latency times and low power consumption. In this work, we present a proposal of a photonic device that is designed to implement one basic deep learning architecture, the multilayer perceptron, which consists of the sequential application of matrix-vector multiplications and element-wise nonlinear functions. We analyze how the speed and energy efficiency performance metrics, as well as system requirements, scale with the number of nodes in the layers of the network. Finally, we discuss the possible routes that may enable the implementation of larger networks, which opens a wide variety of research paths that can contribute to the creation of more scalable and applicable photonic devices.
Measuring the Effectiveness of Designing End-User Interfaces Using Design Theories

Software systems are one of the most important technologies that are present in every task that humans and computers perform. Humans perform their tasks by using a computer interface. However, because many developers have not been exposed to one or more courses on Human Computer Interaction (HCI), they sometimes create software using their own preferences based on their skills and abilities and do not consult theories that could help them produce better outcomes. A study was carried out to identity whether software that is developed by using Gestalt Theory combined with interface development principles produces better outcomes compared to software developed using developers' current skills. Results show that participants perceived the system that was developed by a team that had been given training about Gestalt Theory and design guidelines had superior perceived quality compared to another team that did not receive the training. However, results should be taken cautiously.
Providing patient centric healthcare to diabetic patients using D-Care recommender system

Diabetes mellitus is one of the severe diseases prevailing in developing countries like India. High level of sugar in blood and urine leads to diabetes which damages the kidneys, eyes, nerves, blood vessels, heart with potential risk of life threat if not cured in time. Diabetes can be prevented with physical exercises, work out, and healthy diet. Diabetes can also be cured if detected in early stages. However, in today's busy life people tend to ignore physical activity and take unhealthy food causing high glucose level in blood. Patients are generally diagnosed with diabetes in late stages due to which it becomes challenging to cure the disease. To prevent and detect the diabetes in early stages, a solution is needed which can integrated with user's day-to-day life and can continuously monitor their diet/fold and physical activities. In this paper, we are proposing cloud-based recommender system/engine which will further integrate with user's smartphone and wearable devices to prevent diabetes by recommending healthy lifestyle and diet to diabetic patients.
OntoMetrics Evaluation of Quality of e-Government Ontologies

At present, there is a shortage of studies which focus on the analysis of existing e-government ontologies on the web to promote their reuse, maintenance and evolution. This research attempts to fill this gap by identifying and evaluating existing e-government ontologies on the web. Twenty ontologies of the e-government domain are downloaded on the web and a set of quantitative quality metrics adopted from the OntoMetrics ontology evaluation framework are applied to evaluate their schema, knowledge base and graph features. The evaluation results provide insights on the accuracy, understandability, cohesion and conciseness of the e-government ontologies studied.
A Multi-UAS Trajectory optimization Methodology for Complex Enclosed Environments

This paper explores a multi-UAV trajectory optimization methodology for confined environments. One potential application of this technology is performing warehouse inventory audits; this application is used to evaluate the methodologie's impact on minimizing total mission times. This paper investigates existing algorithms and improves upon them to better address the constraints of warehouse-like environments. An existing inventory scanning algorithm generates sub-optimal, collision free paths for multi-UAV operations, which has two sequential processes: solving a vehicle routing problem, and determining optimal deployment time without any collision. To improve the sub-optimal results, this paper introduces three possible improvements on the multi-UAV inventory tracking scenario. First, a new algorithm logic which seeks to minimize the total mission time once collision avoidance has been ensured rather than having separate processes. Next, an objective function that seeks to minimize the maximum UAV mission time rather than minimizing the total of all UAV mission times. Last, an operational setup consisting of multiple deployment locations instead of only one. These algorithms are evaluated individually and in combination with one another to assess their impact on the overall mission time using a representative inventory environment. The best combination will be further analyzed through a design of experiments by varying several inputs and examining the resulting fleet size, computation time, and overall mission time.
Dynamic Multi-swarm Global Particle Swarm Optimization

This paper proposes a dynamic multi-swarm global particle swarm optimization (DMS-GPSO) that consists of two novel strategies to balance the exploration and exploitation abilities. In DMS-GPSO, the entire population is divided into a global sub-swarm and dynamic multiple sub-swarms. During the evolutionary process. the global sub-swarm focus on exploitation under the guidance of the optimal particle in the entire population, while the dynamic multiple sub-swarms focus on exploration under the guidance of the neighbors best-so-far position. Moreover, the store-reset strategy of the global sub-swarm is applied to save computational resource and increase population diversity, aiming to improve the exploration ability of DMS-GPSO at the initial evolutionary stage. At the later evolutionary stage, some favorable particles of the global sub-swarm stored in an archive are combined with particles in the DMS sub-swarms as a single population to search for optimal solutions, intending to enhance the exploitation ability. The comparison results between DMS-GPSO and other 7 peer algorithms on the CEC2013 test suite demonstrate that DMS-GPSO can avoid the premature convergence when solving multimodal problems, and yields more effective performance in complex problems.
Image Classification Based on Image Knowledge Graph and Semantics

Since the ImageNet competition was held in 2012, the machine learning algorithm has performed very well on the image classification task. In object classification task, there are still some problems. For example, similar categories are difficult to be distinguished in images. In addition, with the increasing number of object categories, the background of scene has become another crucial issue in object classification. This paper focuses on image object recognition and makes two major contributions to tackle these issues. Firstly, we propose a semantic refinement method that analyzes the relationship among similar categories in images from the perspective of semantic knowledge. The knowledge of the relationship between semantics comes from a wide range of open knowledge. Secondly, we utilize the knowledge graph method to create the image knowledge graph with multiple categories in images. Our method exploits the knowledge from the adjacency matrix computed on train data to merge relevant classes into graph. We conduct extensive experiments on large-scale image datasets (ImageNet), demonstrating the effectiveness of our approach. Further, our method participates in ILSVRC 2012 challenges, and obtain the new state-of-the-art results on the ImageNet (82.43%).
Similarity of Continuous Optimization Problems from the Algorithm Performance Perspective

In the field of optimization, the performance of algorithms can be inferred by relating the problem features or properties to the past performance of algorithms on the tested instances of problems. However, the problem features or properties are usually strongly connected to the specific problem domain, which makes it difficult to apply the established feature-performance model in one problem domain to another. In our previous work, we tested 28 algorithms from different categories on a problem set consisting of 5562 instances and mapped the algorithm performance into a vector of 5562 elements via the proposed fractional ranking method. In this work, we propose to use the fractional ranking consisting of performance mapping of three unique criteria for finding similarity between continuous optimization problems. A feature vector consisting of 28 elements, derived from the performance of 28 algorithms, is used to compare the problems. This methodology enables comparison and visualizes differences of performance by the algorithms applied to the problems from different domains. The principal component analysis shows that the selected problem set has comprehensive coverage of the instance space. The clustering analysis shows that performance-wise speaking, the instances from different problem families or dimensions may be very close in the instance space.