Reviewerly/exHarmony huggingface dataset:
Speculative and Critical Design in Education

This full-day, interdisciplinary workshop will address the use of Speculative and Critical Design (SCD), Design Fiction, and related practices, including those that use provocation, ambiguity and activism, within undergraduate, postgraduate and professional educational contexts. The day will include the sharing of experiences of working with these methods within the classroom, along with discussions around the perspectives and motivations underpinning the choice of approaches. The aims of the workshop organizers are to share and support effective practices in education, and to further the debate on the future of Speculative and Critical Design within educational curricula. The workshop builds on the activities of the current SpeculativeEdu project, which is concerned with developing novel educational skills and design practices for the 21st century, especially those focused on critical relations between technology and human society.
Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

This paper addresses the development and implementation of an obstacle avoidance strategy for a multi-robot system operating in an unknown environment. This novel strategy is based on the conventional Bug-1 obstacle avoidance algorithm, which is a non-heuristic method for obstacle avoidance in an unknown environment. In the Bug-1 algorithm, a robot circumnavigates the obstacle to find the coordinates of the point, having minimum distance to the goal. In the case of the new strategy, two robots will circumnavigate the obstacle in such a manner that it will reduce both the total travel time and the distance traveled. Information acquired by the individual robots during the circumnavigation is shared across other robots to accomplish the obstacle avoidance efficiently. A theoretical analysis is carried out to show the improvement in travel time and energy expenditure of the robots in implementing the new strategy. Different test scenarios for comparing the performance of the obstacle avoidance strategies using simulations is also identified. The simulation studies using these scenarios suggest that the new algorithm is a better algorithm with respect to multi-robot obstacle avoidance. The experimental study conducted also shows that robots using this new algorithm have a better travel time and less energy expenditure than the conventional Bug-1 algorithm.
Drift and Erosion in Software Architecture

Disciplined practice of software architecture, and the broader encompassing discipline of software design, is fundamental to a project's success in every stage of the software life cycle. Choosing and implementing an appropriate architecture maximizes team velocity by structuring a project to meet business needs and adhere to quality standards. One enemy of good architecture is architecture drift, where software systems implement an architecture that is not specified. Even worse, architecture erosion threatens projects in the form of design decisions that violate prescriptive architecture as well as quality attributes. In this paper, we will discuss the importance of a reliable architecture, the symptoms of architecture drift and erosion, and some of the existing tools and methods used to combat them.
Normal high order elements in finite field extensions based on the cyclotomic polynomials

We consider elements which are both of high multiplicative order and normal in extensions \(F_{q^{m} } \) of the field \(F_{q} \). If the extension is defined by a cyclotomic polynomial, we construct such elements explicitly and give explicit lower bounds on their orders.
A Robot in the Library

If robots are to be beneficial and appealing within an international library setting, useful to patrons and cooperative with library personnel, then library culture becomes an important issue. Human-robot interaction designers need to consider technological factors, the library culture, as well as expectations from users to develop a solution that could withstand the fall of time and not atone when enthusiasm is lost. We reviewed the recent literature and performed qualitative analyses of our findings to explore the tasks that are proven to have been robotized in a library, and to investigate current cultural and technological barriers that would decrease acceptance rates of robots in a library. Search of Scopus, Web of Science, IEEEXplore, and LISTA databases was conducted complemented with Google searches. Articles with scientific content were included if they described the use of a robot in a library setting, were written in English, and were published within 2016–2018. We identified 1037 references and after title, abstract and full-text screening according to the eligibility criteria we included 18 records in our analysis. We summarize the main roles of library robots as: robots for navigation, book location and placement; robots as information desks; and robots in education. Barriers towards robotic acceptance was found to be: anxiety and fear among librarians of being replaced by robots; the lack of resources (time, money, space) for maintaining a robot and the cost of organizational restructuring; maintaining the enthusiasm around it over time; and the patrons' need for human contact.
Face Recognition Using PCA Implemented on Raspberry Pi

Currently, many consumers electronic are utilizing personal identification technique such as ID, fingerprint, face, Iris and palmprint recognition for security reason. Among other biometric traits, face image is easy to capture using Pi Camera. This paper proposes facial recognition using principle component analysis (PCA) method implemented using Raspberry Pi embedded processor. The algorithm is model using PC based method to identify the best eigenvector to represent facial image. The pre-calculated statistical parameter is then used to implement the PCA algorithm in Raspberry Pi embedded processor. OpenCV image processing libraries is use to support the basic task of image pre-processing such as cropping, resize and color conversion. In the propose work, the whole system is implemented in low cost processor to evaluate the performance in terms of recognition rates and processing time. The proposed method includes three main phases. The pre-processing phase processes facial ORL image and to collect significant information. The second phase extracts important features from image and decreases the size of the image using the PCA method. This phase uses a linear projection technique to decrease redundancies and remove noise from the image. Moreover, this strategy also improves the strength of discrimination power in the feature space. The Euclidean distance classifier is utilized in the third phase of classification. The best recognition rates achieved using the propose method is 82.5% implemented using low cost embedded system.
Conversational semantic parsing over tables by decoupling and grouping actions

In this article, we present a new approach for answering a series of simple but inter-related questions over a table by mapping them to the corresponding SQLs. We follow the sequence-to-action paradigm and contribute two innovations: (1) an improved action space. We decouple some actions from existing work because they were jointly deciding unrelated query components, and group some actions with one neural network feature extractor as they were seeking the same aspect of information from the question and table. We also extend the equal action to match multiple cells under a column with a key word or phrase. (2) a simplified scoring model. For question-table encoder, we incorporate BERT to enhance the detection of reference and ellipsis, thus better resolve conversations. For action decoder, we adopt simple feed-forward neural networks to avoid heuristically designed features. We achieve competitive performance on the SequentialQA dataset. Further studies show the effectiveness of each part proposed in our approach.
A hybrid-security model for privacy-enhanced distributed data mining

This study encompasses the proposal of a novel hybrid-security model that incorporates the benefits of both the centralised data mining system as well the distributed data mining system. The hybrid model provides two levels of security. The first security level perturbs the individual datasets by transforming it into a non-understandable form using the four-dimensional rotation transformation, and the second security level helps in performing secure distributed data mining using the ratio of secure summation protocol. The trivial data mining techniques, such as k-means clustering technique and naïve Bayes classification technique, verifies the efficiency and accuracy of the hybrid security model. The hybrid security model provides security to sensitive data without compromising the quality of the data. The accuracy obtained in classification task and clustering task using naïve Bayes and k-means technique is high when different datasets in a privacy-enhanced distributed data mining environment verify the working of the hybrid security model.
Archive Update Strategy Influences Differential Evolution Performance

In this paper the effects of archive set update strategies on differential evolution algorithm performance are studied. The archive set is generated from inferior solutions, removed from the main population, as the search process proceeds. Next, the archived solutions participate in the search during mutation step, allowing better exploration properties to be achieved. The LSHADE-RSP algorithm is taken as baseline, and 4 new update rules are proposed, including replacing the worst solution, the first found worse solution, the tournament-selected solution and individually stored solution for every solution in the population. The experiments are performed on CEC 2020 single objective optimization benchmark functions. The results are compared using statistical tests. The comparison shows that changing the update strategy significantly improves the performance of LSHADE-RSP on high-dimensional problems. The deeper analysis of the reasons of efficiency improvement reveals that new archive update strategies lead to more successful usage of the archive set. The proposed algorithms and obtained results open new possibilities of archive usage in differential evolution.
Accuracy Requirements For Cooperative Radar With Sensor Fusion

Reliability and robustness are the essential requirements for automotive radar systems. However an automotive radar system suffers from environmental conditions and interference. Applying a proper radar data fusion algorithm can significantly increase the estimation quality and robustness. This article investigates the accuracy requirements based on the geometric parameters for cooperative radar sensors with central sensor fusion. The results show that a radar sensor fusion for cooperative radar can increase the estimation quality and therefore the system robustness by using at least two radar sensors. Furthermore, results show that three to four sensors at a minimum distance of two to four meters are sufficient for high quality estimation. At the same time the system requirements regarding the accuracy for every single radar sensor can be relaxed to a range accuracy as low as one meter standard deviation.
User eXperience Heuristics for National Park Websites

A national park website extends (in certain way) a physical national park. It does not have a physical location so its elements and information can be appreciated from anywhere in the world. Usability is a well-known concept, for decades. User eXperience (UX) is a broader concept that encompasses usability. The usability and UX evaluation are important tasks to perform when developing any kind of websites. In this regard it is important to evaluate whether a national park website is intuitive, easy to use, and allows users to complete their objectives. National park websites have their own features that differentiate them from other systems, so it is necessary to use a set of appropriate heuristics for these types of websites. The article presents a set of 14 heuristics to evaluate the UX of national park websites. The heuristics were developed using the methodology proposed by Quiñones et al. [2]. To validate and refine the UX heuristics for national park websites, we performed two iterations. In the first iteration the heuristics were validated through heuristic evaluation and expert judgment (survey), while in the second iteration the heuristics were validated through user tests (co-discovery and focus group). Based on validation results, we concluded that the proposed set of heuristics are effective.
Home as a Platform: Levels of Automation for Connected Home Services

Tomorrow’s home will not only be a place to live, but will be a platform of services and experiences provided by an interconnected ecosystem of technologies. This platform requires a framework that describes and simplifies the types of homes and services which address consumers’ needs and provides current and future directions for researchers and practitioners. Although there have been some efforts to understand homes’ types and levels of automation, there is not a commonly established framework. In this paper, we propose a framework outlining types of homes and various levels of home automation: Electric homes, Customized homes, Proactive homes, Support homes, and Companion homes. This framework is built upon previous automation models and is supported by our expert interview panel. The first two levels describe current states and the next three levels discuss future directions and possibilities. Each level varies in terms of the complexity and intelligence of in-home technologies, their interaction and integration with one another as well as with the user, ability to process information based on the stages from information processing theory, and aspects of companionship that a home can provide to its residents. The proposed framework is the first step in achieving a standardized understanding of home automation across stakeholders.
Real Time Pedestrian Detection Using Modified YOLO V2

Object detection is an important task in autonomous driving. Identifying an object and localizing it in the given 2D space is comparatively easier task for humans but for an autonomous vehicle, the case is not the same. Recently, many algorithms were proposed to mitigate these issues out of which YOLO (You Only Look Once) is out of the box in a sense that it can run faster without compromising the accuracy. Pedestrian detection is the most difficult and critical application in automotive perspective since any error can be fatal. In this paper, pedestrian detection and localization is mainly focused using our modified YOLOv2 architecture (Model H). Our architecture (Model H) results were compared with the YOLOv2 baseline model's results for well-known INRIA person dataset and our approach yield better results. Also implemented it in embedded system hardware using NVIDIA Jetson TX1 and Zed Stereo camera with 30 fps detection rate. Apart from detection, depth is also estimated using the ZED camera and depth map is created.
Security vulnerability in Internet of Things sensor networks protected by Advanced Encryption Standard

In the new era of Internet of Things, the emerging of smart devices makes security and privacy the first requirements and the major challenges of a distributed network. Despite the implementation of security measures, as encryption mechanisms protecting sensor data, and cryptographic algorithms, various attacks seem to undermine the IoT devices security. This paper reports the preliminary results of a side-channel attack (scatter attack) addressed on an 8-bit IoT microcontroller protected by the Advanced Encryption Standard. The attack, based on an high-SNR data acquisition micro-system and a suitable statistical analysis, allows to discover part of the encryption key, demonstrating the security vulnerability of Internet of Things sensor networks protected by the AES.
MRCAT: In Situ Prototyping of Interactive AR Environments

Augmented reality (AR) blends physical and virtual components to create a mixed reality experience. This unique display medium presents new opportunities for application design, as applications can move beyond the desktop and integrate with the physical environment. In order to build effective applications for AR displays, we need to be able to iteratively design for different contexts or scenarios. We present MRCAT (Mixed Reality Content Authoring Toolkit), a tool for in situ prototyping of mixed reality environments. We discuss the initial design of MRCAT and iteration after a study ( $$N = 14$$ ) to evaluate users’ abilities to craft AR applications with MRCAT and with a 2D prototyping tool. We contextualize our system in a case study of museum exhibit development, identifying how existing ideation and prototyping workflows could be bolstered with the approach offered by MRCAT. With our exploration of in situ prototyping, we enumerate key aspects both of AR application design and targeted domains that help guide design of more effective AR prototyping tools.
Automatic generation of lexica for sentiment polarity shifters

Abstract Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters . Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not , no , and without . A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.
The Genesy Model for a Blockchain-based Fair Ecosystem of Genomic Data

Recent advances in technology have drastically downsized costs and implementation times for genomic services. The wide availability of low-cost genomic technologies and the easy access to genomic data can significantly improve the productivity and efficiency of healthcare, all to the benefit of social well-being in general. For example, by creating the conditions for researchers to identify the causes of multiple diseases and by contributing to the development of new drugs, we can improve the quality of life and give people, as users of genomic services, the means to positively impact their health. This article describes how blockchain technology can lay the foundations of an ecosystem that encourages users to acquire and share their genomic data in full awareness and without fear of being circumvented, so as to participate in the benefits and advances in genomic research. The starting point is Genesy, an innovative blockchain platform that transcribes genomic data, thus facilitating and at the same time safeguarding the users of genomic services in their relationships with parties interested in accessing and using data they own, such as research centers, pharmaceutical companies, hospitals, geneticists. This result is obtained by exploiting the capabilities of blockchain technology to notarize data and prevent their unauthorized use, and at the same time to make them objects of possible transactions between different parties. Looking ahead, the Genesy model can be generalized to promote an ecosystem, and a fair market, of all types of biomedical data.
A New Texture Descriptor: The Homogeneous Local Binary Pattern (HLBP)

This paper presents a simple and novel descriptor named Homogeneous Local Binary Pattern (HLBP) for texture analysis. The purpose of this description is to improve the Local Binary Pattern (LBP) approach basing on the impact of criterion homogeneous region using General Adaptive Neighborhood (GAN) principle. HLBP method is generated by using the criterion homogeneity which helps to represent a significant feature based on relationships between neighboring pixels. The main idea of HLBP is to threshold the distance between the current pixel and each of its neighbors with a homogeneity tolerance value which correspond more to the underlying spatial structures consequently allow extracting highly distinctive invariant features of the image. To assess the performance of the our proposed descriptor, we use "Outex" database and compared with the basic (LBPs). The experimental results show that the proposed Homogeneous Local Binary Pattern gives a good performance in term of classification accuracy.
A 3D Scanning System for Inverse Analysis of Moist Biological Samples: Design and Validation Using Tendon Fascicle Bundles

In this article, we present the design and validation of a non-contact scanning system for the development of a three-dimensional (3D) model of moist biological samples. Due to the irregular shapes and low stiffness of soft tissue samples, the use of a non-contact, reliable geometry scanning system with good accuracy and repeatability is required. We propose a reliable 3D scanning system consisting of a blue light profile sensor, stationary and rotating frames with stepper motors, gears and a five-phase stepping motor unit, single-axis robot, control system, and replaceable sample grips, which once mounted onto the sample, are used for both scanning and mechanical tests. The proposed system was validated by comparison of the cross-sectional areas calculated based on 3D models, digital caliper, and vision-based methods. Validation was done on regularly-shaped samples, a wooden twig, as well as tendon fascicle bundles. The 3D profiles were used for the development of the 3D computational model of the sample, including surface concavities. Our system allowed for 3D model development of samples with a relative error of less than 1.2% and high repeatability in approximately three minutes. This was crucial for the extraction of the mechanical properties and subsequent inverse analysis, enabling the calibration of complex material models.
Entropy-Shield:Side-Channel Entropy Maximization for Timing-based Side-Channel Attacks

The hardware systems have experienced a plethora of side-channel attacks (SCAs) in recent years with cache-based SCAs being one of the dominant threats. The SCAs exploit the architectural caveats, which invariably leak essential information during an application's execution. Shutting down the side-channels is not a feasible approach due to various restrictions, such as architectural changes and complexity. To overcome such concerns and protect the data integrity, we introduce Entropy-Shield in this work. The proposed Entropy-Shield aims to maximize the entropy in the leaked side-channel information rather than attempting to close the side-channels. To achieve this, the proposed Entropy-Shield introduces carefully and sensibly crafted perturbations into the victim application, thereby increasing the entropy of the information obtained by the attacker to deduce the secret key, while the information being observed looks legit yet futile. This methodology has been successfully tested on cache targeted SCAs such as Flush+Reload and Flush+Flush and the key information retrieved by the attacker is shown to be ultimately futile, indicating the success of proposed Entropy-Shield.