Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Secure Authentication Scheme for Medical Data Sharing

Data security is vital for medical cyber physical system (MCPS). The decentralization feature of blockchain is helpful to solve the problem that the secure authentication process is highly dependent on the trusted third party and implement data security transmission. In this paper, the blockchain technology is used to describe the security requirements in authentication process. A network model of MCPS based on blockchain is proposed. Through analysis of medical data storage architecture, data was ensured not to be tampered and trackable. The security threat was eliminated by bilinear mapping in the authentication process of medical data providers and users. The credibility problem of the trusted third party was avoided and the two-way authentication was realized between the hospital and blockchain node. The security analysis and performance test were carried out to verify the security and related performance of the authentication protocol. The results show that the MCPS based on blockchain realizes medical treatment data sharing, and meets safety requirements in the security authentication phase.
When actions speak louder than clicks

Maximizing sales and revenue is an important goal of online commercial retailers. Recommender systems are designed to maximize users' click or purchase probability, but often disregard users' eventual satisfaction with purchased items. As result, such systems promote items with high appeal at the selling stage (e.g. an eyecatching presentation) over items that would yield more satisfaction to users in the long run. This work presents a novel unified model that considers both goals and can be tuned to balance between them according to the needs of the business scenario.
UAV 3-Dimension Flight Path Planning Based on Improved Rapidly-exploring Random Tree

The traditional RRT algorithm is difficult to realize the 3-dimensional flight path planning of unmanned aerial vehicle (UAV). In view of the complexity and particularity of the 3-dimensional environment, an improved RRT algorithm is proposed. According to the specific task requirements and the performance limitation of UAV, the flight height constraint, the maximum range constraint and the pitch angle constraint are designed. A new node acceptance criteria and path adjustment method is proposed for the 3-dimensional environment. From the theoretical point of view, the convergence of the improved algorithm is analyzed. On the other hand, a 3-dimensional simulation environment is established for algorithm test. The simulation results show that the improved RRT algorithm can realize the path planning in three-dimensional environment and meet the mission requirements of UAV.
Nighthawk: Transparent System Introspection from Ring -3

During the past decade, virtualization-based (e.g., virtual machine introspection) and hardware-assisted approaches (e.g., x86 SMM and ARM TrustZone) have been used to defend against low-level malware such as rootkits. However, these approaches either require a large Trusted Computing Base (TCB) or they must share CPU time with the operating system, disrupting normal execution. In this paper, we propose an introspection framework called Nighthawk that transparently checks system integrity at runtime. Nighthawk leverages the Intel Management Engine (IME), a co-processor that runs in isolation from the main CPU. By using the IME, our approach has a minimal TCB and incurs negligible overhead on the host system on a suite of indicative benchmarks. We use Nighthawk to check the integrity of the system software and firmware of a host system at runtime. The experimental results show that Nighthawk can detect real-world attacks against the OS, hypervisors, and System Management Mode while mitigating several classes of evasive attacks.
Internet of Things–triggered and power-efficient smart pedometer algorithm for intelligent wearable devices

Today wearable devices can be used to monitor the health of patients, among other uses. This chapter proposes an intelligent wearable device (IWD) with a pedometer algorithm that detects and records physical movement such as walking. This device uses a smart algorithm for IWDs for step detection that analyzes the instantaneous acceleration versus time waveform and can detect the specific movements involved in stepping. The results of the algorithm are discussed with different cases and threshold values considered. This algorithm uses a mobile app using the Internet of Things to keep track of steps, and the Gaussian noise is removed from the accelerometer data. This power-efficient pedometer algorithm can be used for IWDs.
How to predict recommendation lists that users do not like

It is obviously that many user-object online rating systems usually contain the information of the users’ attitudes: like or dislike the objects, these systems can be represented by signed bipartite networks. The common recommendation systems work on unsigned networks. Even if some consider the negative edges, they are all concerned with the objects that the recommended user likes. However, the objects that the user does not like are more personalized. Based on Network-Based Inference (NBI) and signed bipartite networks, we proposed Signed Network-Based Inference (SNBI) to provide the negative recommendation list, which predicts the objects that users dislike. The SNBI algorithm includes two mechanisms: When allocating resources on signed bipartite networks, first method (SNBI-1) does not allocate resources while second method (SNBI-2) reduces resources if there is a negative edge. By comparing the results on the actual data sets with NBI, we found that SNBI-2 which takes into account the role of the negative edges can better predict the objects that user does not like while maintaining the validity of the positive recommendation list, then gives more personalized recommendation.
Audio augmented reality for human-object interactions

In the past, augmented reality (AR) research focused mostly on visual augmentation, which requires a visual rendering device like head-mounted displays that are usually obtrusive, expensive, and socially unaccepted. In contrast, wearable audio headsets are already popularized and the auditory sense also plays an important role in everyday interactions with the environment. In this PhD project, we explore audio augmented reality (AAR) that augments objects with 3D sounds, which are spatialized virtually but are perceived as originating from real locations in the space. We intend to design, implement, and evaluate such AAR systems that enhance people's intuitive and immersive interactions with objects in various consumer and industrial scenarios. By exploring AAR using pervasive and wearable devices, we hope to contribute to the vision of ubiquitous AR.
Discriminative globality-locality preserving extreme learning machine for image classification

Extreme learning machines (ELM) have been widely used in classification due to their simple theory and good generalization ability. However, there remains a major challenge: it is difficult for ELM algorithms to maintain the manifold structure and the discriminant information contained in the data. To address this issue, we propose a discriminant globality-locality preserving extreme learning machine (DGLELM) in this paper. In contrast to ELM, DGLELM not only considers the global discriminative structure of the dataset but also makes the best use of the local discriminative geometry information. DGLELM optimizes the projection direction of the ELM output weights by maximizing the inter-class dispersion and minimizing the intra-class dispersion for global and local data. Experiments on several widely used image databases validate the performance of DGLELM. The experimental results show that our approach achieves significant improvements over state-of-the-art ELM algorithms.
Assistive wearables

Wearable technologies are body-worn devices, including smart clothes, e-textiles, and accessories. Wearables embed computational capabilities in garments to provide information, services and resources for end users. By being continuously worn, promptly accessible, and unobtrusive, wearable devices are well-suited to serve as assistive technologies meeting the needs of users with diverse abilities. For neurodiverse users specifically, belts have been explored to control impulsive speaking, smart clothes (such as t-shirts and caps) have been employed for emotional expression, and badges have been assessed to raise proximity awareness. Among diverse form factors, wrist-worn devices stand out due to their conventional look and popular usage. They have been explored for emotional regulation, touch monitoring, stress release, and self-regulation. Despite a growth in research and development of wearables, their application as assistive technologies for neurodiverse users is still underexplored. Challenges emerge on designing for heterogeneous users' profiles, automating assistance, increasing accuracy, and managing users' privacy. This paper discusses the main opportunities and affordances for wearables to support neurodiverse users as well as the grand challenges in the field.
StoryTime

We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences. By building on existing solutions related to both visual interfaces and book recommendation strategies for children, StoryTime can generate suggestions without historical data or adult guidance. We discuss the benefits of StoryTime as a starting point for further research exploring the cold start problem, incorporating historical data, and needs related to children as a complex audience to enhance the recommendation process.
Express-Lane Scheduling and Multithreading to Minimize the Tail Latency of Microservices

Managing high-percentile tail latencies is key to designing user-facing cloud microservices. A main contributor to end-to-end tail latency is queuing, wherein nominal tasks are enqueued behind rare, long ones, due to head-of-line blocking. In this paper, we propose Express-Lane SMT (ESMT), which extends the hardware scheduling of a simultaneously multithreaded (SMT) core to provide an "express-lane" execution context for short tasks, protecting them from queuing behind rare, long ones. As tasks reach predefined service cutoffs, ESMT preempts and migrates them to the subsequent queue to be serviced by the next SMT execution lane, thereby preventing Head-of-Line (HoL) blocking. We further propose an enhanced variant of ESMT that allows execution lanes to work-steal from each other to maximize utilization. Our evaluation shows that ESMT with work stealing reduces tail latency over a conventional SMT core by an average of 56% and 67% under moderate (40%) and high (70%) system loads, respectively.
Inter-class angular margin loss for face recognition

Increasing inter-class variance and shrinking intra-class distance are two main concerns and efforts in face recognition. In this paper, we propose a new loss function termed inter-class angular margin (IAM) loss aiming to enlarge the inter-class variance. Instead of restricting the inter-class margin to be a constant in existing methods, our IAM loss adaptively penalizes smaller inter-class angles more heavily and successfully makes the angular margin between classes larger, which can significantly enhance the discrimination of facial features. The IAM loss can be readily introduced as a regularization term for the widely-used Softmax loss and its recent variants to further improve their performances. We also analyze and verify the appropriate range of the regularization hyper-parameter from the perspective of backpropagation. For illustrative purposes, our model is trained on CASIA-WebFace and tested on the LFW, CFP, YTF and MegaFace datasets; the experimental results show that the IAM loss is quite effective to improve state-of-the-art algorithms.
Coexistence of Discrete-Variable QKD with WDM Classical Signals in the C-Band for Fiber Access Environments

In this paper, a coexistence scheme between a Discrete-Variable Quantum Key Distribution (DV-QKD) and four bidirectional classical channels in a Passive Optical Network (PON) topology is theoretically investigated. The study aims to explore the imposed limitations considering the coexistence of weak quantum channels with realistic traffic flows of classical streams through shared fiber infrastructures. Based on a `plug and play' phase coding DV-QKD implementation, we conducted numerical simulations of the QBER and the secure key rate for fiber distances up to 10 km. The reported results suggest that in a fixed C-band grid, the spectral isolation between classical and quantum channels is essential at dense grids. By removing the leakage noise through stronger spectral isolation, the photons linked with the Raman scattering become the dominant noise source, since this mechanism covers an ultra-broadband window and gets stronger as the propagation distance increases.
Value-Oriented Requirements: Eliciting Domain Requirements from Social Network Services to Evolve Software Product Lines

Social network services allow a large population of end-users of software products to publicly share their concerns and experiences about software systems. From a software engineering perspective, such data can be collected and analyzed to help software development organizations to infer users’ emerging demands, receive their feedback, and plan the rapid evolution of software product lines. For the evolution of software product lines, organizations supplement emerging requirements in their products to meet user’s needs and also to retain their dominance in the market. Therefore, social network services, being a communication channel, have supported a number of software development activities such as requirements engineering. It has supported software development organizations to cope with numerous limitations of the traditional requirements engineering approaches by eliciting, prioritizing, and negotiating user requirements. However, these approaches do not consider eliciting requirements in terms of variability and commonality while identifying requirements. To address this issue, we have proposed a social network service-based requirement engineering process. It considers the attributes of users’ opinions to determine variability and commonality. In order to justify our proposed approach, a controlled experiment was conducted on a sample set of end-users on Facebook and Twitter. The experimental results show that the team using the proposed approach performed better in terms of efficiency and effectiveness than the team that used a traditional requirements engineering approach.
Main Considerations in Elaborating Organizational Information Security Policies

With the increasing reliance on information technologies, cloud services and internet as communications media, businesses, public and societal organizations face growing threats from cyberspace and, respectively, demands to protect sensitive data and information they collect, use, and disseminate. This paper elaborates on the key considerations organisations with more limited resources, such as schools, universities, research institutes and public organizations need to take into account in designing and implementing a respective information security policy. We start with a description of context and definition of the scope of information security policy, in particular delineating 'information' and 'cyber' security, and provide an overview of the most prominent frameworks and standards. On that basis we elaborate and structure the main areas of an information security policy, the main implementation challenges, and the need to review and amend the policy in a continuous cycle and comprehensive risk management framework. Depending of the specifics of their work, any school, university, institute and municipality may use this elaboration as a starting point in devising its own information security policy.
A Recursive Algorithm for Computing Inferences in Imprecise Markov Chains

We present an algorithm that can efficiently compute a broad class of inferences for discrete-time imprecise Markov chains, a generalised type of Markov chains that allows one to take into account partially specified probabilities and other types of model uncertainty. The class of inferences that we consider contains, as special cases, tight lower and upper bounds on expected hitting times, on hitting probabilities and on expectations of functions that are a sum or product of simpler ones. Our algorithm exploits the specific structure that is inherent in all these inferences: they admit a general recursive decomposition. This allows us to achieve a computational complexity that scales linearly in the number of time points on which the inference depends, instead of the exponential scaling that is typical for a naive approach.
The Fixed Point Property of Non-Retractable Topological Spaces

Unlike the study of the fixed point property (FPP, for brevity) of retractable topological spaces, the research of the FPP of non-retractable topological spaces remains. The present paper deals with the issue. Based on order-theoretic foundations and fixed point theory for Khalimsky (K-, for short) topological spaces, the present paper studies the product property of the FPP for K-topological spaces. Furthermore, the paper investigates the FPP of various types of connected K-topological spaces such as non-K-retractable spaces and some points deleted K-topological (finite) planes, and so on. To be specific, after proving that not every one point deleted subspace of a finite K-topological plane X is a K-retract of X, we study the FPP of a non-retractable topological space Y, such as one point deleted space Y ∖ { p } .
Mobile Robotic Radiation Surveying Using Recursive Bayesian Estimation

Nuclear facilities require wide-area surveys and remote response to the detection of abnormal radiation levels. These typically require a large number of measurement locations using fixed search patterns. Such approaches are time-consuming, require extended radiation exposure, and are difficult to routinely replicate by technicians. This paper presents an automated method of detecting and locating single or multiple small gamma-ray sources in an unstructured environment, requiring significantly fewer measurements than traditional methods and without a need for post-processing. A mobile robot can collect higher-precision data than practically possible by a human and removes the technician from the radiation area. This is enabled by addressing complexities that previously made automation difficult including supervisory control, obstacle avoidance, sensor positioning over a large height range, recognizing environmental complexities (shielding, etc and modifying survey parameters based on aberrant readings. The developed solution uses a mobile platform with a height-adjustable (up to 2.44 meters) radiation detector. Recursive Bayesian Estimation (RBE) is used to update a probability distribution of the location and intensity of source(s) after each measurement. The likelihood function is determined using radiation transport and detector models. Isotopic identification via a gamma library search aids data analysis by distinguishing counts from different sources. Computation considerations are discussed including predicting and localizing multiple sources.
A Multi-label Classification on Topics of Quranic Verses (English Translation) Using Backpropagation Neural Network with Stochastic Gradient Descent and Adam Optimizer

The Quran is a guideline for all Muslims. In the Quran, many things are talked about. In Quranic studies, the Quran is first classified into several topics according to the discussions of the Quranic verses. In this research, a classification model using a Back Propagation Neural Network was built based on the verses of Al-Quran and its multi-labelled topics. This allows the Back Propagation algorithm architecture to issue labels for each class in the form of `yes' or `no' for each output neuron. When using the Back Propagation algorithm, a sentence input that has become a vector is taken. In this way, TF-IDF will be used for feature extraction. Then, the model was evaluated via calculation of Hamming Loss. To ensure an optimal Back Propagation process, a comparison was made between the Stochastic Gradient Descent (SGD) and Adam optimizers. Based on some experiments, the proposed scheme yielded the best performance with a Hamming Loss value of 0.129.
Robust Crypto-Governance Graduate Document Storage and Fraud Avoidance Certificate in Indonesian Private University

In this study, we considered crypto-governance as a solution for critical problems in private university management: fraud avoidance diplomas, transcripts and diploma supplement. To avoid fraud, it can be done by blockchain technology by the use of University, Company, Traceability, Profit and Policy-making of crypto-governance. Crypto-governance is able to use IBM using hyperledger fabric protocol and private blockchain network. Muhammadiyah Tangerang University (UMT) saved the graduate documents storage manually by human resources. Moreover, it would be wasting time for many tasks, for example when staffs searched database, they would consume a lot of time and the graduate documents were vulnerable to damage in the disaster. Based on the problems above it would face the research questions; how did we keep the graduate documents storage secure and how it could be authentic guaranteed? Blockchain technology with distributed ledger and consensus is considered to overcome this problem. There were 5 steps taken in this research starting from discussing, literature review, analyzing, learning blockchain technology, creating system model and making a conclusion. The conclusions of this study were expected that the data of graduate students could find faster, easily and authentic guaranteed.
Autonomous Navigation of a Small-Scale Ground Vehicle Using Low-Cost IMU/GPS Integration for Outdoor Applications

This paper describes the development of an autonomous navigation system for a small-scale ground vehicle which uses four independent DC motors for propulsion and an Arduino Mega2560 as its onboard computing plataform. The vehicle sensory system is composed of a low-cost Inertial Measurement Unit (which includes 3 magnetometers) and a standard GPS receiver. The onboard computer sends data from the onboard sensors to a Ground Control Station (GCS) and receives navigation commands from it. These commands are then used to drive the ground vehicle's DC motors using PWM (Pulse-Width Modulation) signals. The Ground Control Station software implements an Extended Kalman Filter for sensor data fusion. An ethernet shield and an ethernet wifi bridge are used for wireless communication between the vehicle onboard computer and the Ground Control Station. A two-wheeled vehicle model was used to develop the vehicle controller and to tune the Kalman Filter parameters. Simulations and real-world test results of the proposed autonomous navigation procedure are presented and discussed for a path-following experiment.