Reviewerly/exHarmony huggingface dataset:
User experience study on crane operator erection simulator using senso glove in a virtual reality environment

Abstract Failure or mistakes in operating the crane can cause an accident while working on the project site, mainly when it deals with heavy and extensive material, for example, in the steel erection process. Proper crane operation for steel erection training using Virtual Reality (VR) technology can overcome this problem effectively and efficiently. The level of interactivity in a VR system can be increased by using a controller that can observe the user’s hand and finger movements. Senso Glove is a wireless glove that is able to read hand and finger movements and is able to provide vibrations. In the system developed in this study, Senso Glove is used as a system control tool to read hand movements and become the system input. In addition to input to the system, Senso Glove can also provide vibration to the user when the virtual hand is holding the lever on a crane in a virtual world or when the steel hit some part of construction during the process. Senso Glove’s ability as a crane operator simulator control tool can be tested using the Usability Test. Therefore, USE Questionnaire was used with a Likert Scale as a media survey for system users. From the survey results, it was found that the user was easy to remember how to operate a crane with a Senso Glove with an average value of 4.25 per 5 Likert scales. However, users also find Senso Glove challenging to learn by everyone with an average value of 2.95 per 5 Likert scales. This is due to the precision that is still difficult given by Senso Glove with the average value of virtual hand movements produced by Senso Glove is 3.15 per 5 Likert scale.
Towards making sense of Spark-SQL performance for processing vast distributed RDF datasets

Recently, a wide range of Web applications (e.g. DBPedia, Uniprot, and Probase) are built on top of vast RDF knowledge bases and using the SPARQL query language. The continuous growth of these knowledge bases led to the investigation of new paradigms and technologies for storing, accessing, and querying RDF data. In practice, modern big data systems (e.g, Hadoop, Spark) can handle vast relational repositories, however, their application in the Semantic Web context is still limited. One possible reason is that such frameworks rely on distributed systems, which are good for relational data, however, their performance on dealing with graph data models like RDF has not been well-studied yet. In this paper, we present a systematic evaluation of the performance of SparkSQL engine for processing SPARQL queries. We stated it using three relevant RDF relational schemas, and two different storage backends, namely, Hive, and HDFS. In addition, we show the impact of using three different RDF-based partitioning techniques with our relational scenario. Additionally, we discuss the results of our experiments: (i) we present insights about the trade-offs that characterize different experimental configurations, and (ii) we identify the best and the worst ones for the SP2Bench's benchmark scenario.
The Importance of Virtual Hands and Feet for Virtual Reality Climbing

Virtual reality (VR) climbing systems registering physical climbing walls with immersive virtual environments (IVEs) have been a focus of past research. Such systems can provide physical user experiences similar to climbing in (extreme) outdoor environments. While in the real world, climbers can always see their hands and feet, virtual representations of limbs need to be spatially tracked and accurately rendered in VR, which increases system complexity. In this work, we investigated the importance of integrating virtual representations of the climber's hands and/or feet in VR climbing systems. We present a basic solution to track, calibrate and represent the climber's hands and feet, and report the results of a user study, comparing the importance of virtual limb representations in terms of perceived hand and feet movement accuracy, and enjoyability of the VR climbing experience. Our study suggests that the inclusion of feet is more important than having a hand visualization.
A Novel Adaptive Multi-View Non-Negative Graph Semi-Supervised ELM

This paper represents a semi-supervised learning framework, which integrates multi-view learning, extreme learning machine (ELM) and graph-based semi-supervised learning. The aim is to expand the scope of adaptation of non-negative sparse graph (NNSG) framework, under a multi-view condition and a non-linear relationship. The proposed multi-view learning method will be adaptive since when data is single-view the framework will degenerate into an embedded framework for NNSG framework. The proposed ELM method also will be adaptive since the number of hidden layer neuron will change with different number of input and output layer neuron. The combination of both proposed methods outperforms traditional graph-based semi-supervised learning, such as flexible manifold embedding (FME) and NNSG framework, which can not establish an affinity matrix for multi-view and can not establish a non-liner model for unknown data. Unlike traditional graph-based semi-supervised learning methods, which only can label propagation and build linear regression models for single or multi-view data, our proposed method has an obvious advantage that is applicable to any single or multi-view data, and builds linear or non-linear models. We provides extensive experiments on four public database in order to evaluate the performance of the proposed method. These experiments demonstrate significant improvement over the state-of-the-art algorithms in label propagation and processing of new data.
On the Use of Design Thinking: A Survey of the Brazilian Agile Software Development Community

Design Thinking (DT) has been chosen as an approach to support problem-solving by many software development companies. However, there are divergences between the professionals of these companies concerning which techniques are performed, which steps are followed, and the way to implement this approach, as it proposes itself, to be divergent to generate numerous alternatives and, also, convergent, to find a solution. For this reason, aiming to characterize how the software companies have been implemented DT, this paper presents the results of a survey answered by 127 professionals from the Brazilian software industry. The results report a variety of scenarios in which DT has been applied: more than ten different models (sets of steps) are followed by the professionals; more than 50 techniques have been used, mainly, for meeting the needs in the process, according to the context of use and based on previous experiences. We also present 29 computational tools that, according to the respondents, assist the execution of DT, in addition to the integration with agile methods, allowing them to generate ideas and solutions, to explore and understand the problem.
Whither Humane-Computer Interaction? Adult and Child Value Conflicts in the Biometric Fingerprinting for Food

This paper reports on the value conflicts that beneficiaries experience when engaging in the monthly ritual of the biometric authentication of their fingerprints to claim state-sponsored food entitlements in India. Drawing on value-based orientations to HCI inquiry, the study locates the interactions around the biometric process to illustrate the ways in which beneficiaries find their values of time, dignity, and privacy, consistently disregarded by the interactive demands of the biometric system. Additionally, to cope with these value conflicts, some beneficiaries pass on the responsibilities of completing the biometric process to the children in their families. While adult beneficiaries are vocal and articulate about the value tensions in their lives, children cope with the anxieties of interacting with the biometric process, silently; even as they experience conflicts in their education, play, and study time.
Engaging with Public Art

At its best, public art can promote moral learning in individuals and societies, and digital technology can help achieve this value. As a first step in creating such systems, this paper presents a probe study exploring the design space of reflective engagement with public art. The probe took the form of a mural journal, which was distributed to participants in Philadelphia. The findings show how public art journaling can be integrated into one's life, both logistically and psychologically, and the value of art journaling for introspection, cultivating attention and having fun. This study surfaces a number of tensions in the design space that designers must navigate, such as the question of reflecting with public art on site (now) versus at home (later). This work provides designers with the grounds for informed inspiration to ideate systems that deepen people's experiences with public art.
Data-driven Multi-level Segmentation of Image Editing Logs

Automatic segmentation of logs for creativity tools such as image editing systems could improve their usability and learnability by supporting such interaction use cases as smart history navigation or recommending alternative design choices. We propose a multi-level segmentation model that works for many image editing tasks including poster creation, portrait retouching, and special effect creation. The lowest-level chunks of logged events are computed using a support vector machine model and higher-level chunks are built on top of these, at a level of granularity that can be customized for specific use cases. Our model takes into account features derived from four event attributes collected in realistically complex Photoshop sessions with expert users: command, timestamp, image content, and artwork layer. We present a detailed analysis of the relevance of each feature and evaluate the model using both quantitative performance metrics and qualitative analysis of sample sessions.
3D Sketching for Multi-Pose Products

During the initial stage of design, 2D perspective sketching is an essential tool for designers. However, for products with multiple moving parts that assume multiple poses during usage, 2D perspective sketching can be painstaking and time-consuming. In this study, we show that such multi-pose products are prevalent in the context of product design and therefore propose a 3D sketching system tailored to multi-pose products. Our system enables designers to sketch 3D curves that can be freely posed and easily viewed from different directions; it makes sketching chained moving parts and propagating changes to different poses and perspectives effortless. We show that, with interactions closely resembling traditional 2D perspective sketching and the physical manipulation of an articulated object, designers can focus solely on ideating, iterating, and communicating multi-pose product concepts during the initial stage of design.
Learning to Listen... On-Device

We have reached an important milestone in Automatic Speech Recognition (ASR) technology, with major industrial AI companies, such as Samsung, Google, Apple, and Amazon releasing high-quality ASR models that run completely on-device, e.g., on consumer smartphones. This is the consequence of giant strides in technological advancements: from making commercial grade ASR systems feasible; to large scale cloud deployments; to the present day state-of-the-art models that run on resource constrained devices.
Visual tracking by dynamic matching-classification network switching

Existing deep trackers can be roughly divided into either matching-based or classification-based methods. The formers are fast but not very robust; while the latter ones introduce more discriminative information but often very slow. In this work, we present a novel real-time robust tracking method to take full use of the benefits from both kinds of networks. First, we propose a matching-classification network switching (MCS) framework to integrate the matching, classification, verification networks and conduct dynamic switching among them. Second, to speed up online update, we devlop a meta learning method as a critical component in our classification network. The meta classifier is trained offline to obtain general discriminative ability and updated online to the current frame just through one iteration. Extensive experiments are conducted on two popular benchmark datasets. Both qualitative and quantitative evaluations show that our tracker performs favorably against other state-of-the-art trackers with real-time performance.
Usability analysis of disaster information systems using usability testing

Abstract The Central Java region is the highest disaster-prone region in Indonesia in the period January - May 2019. The disasters occurred, in total of 554 times, with a total of 14 fatalities, 7 injured, 98,208 affected and displaced. Natural disasters cannot be avoided, but their impact can be minimized with good management. Disaster management and anticipation include utilizing information systems. A good information system is a system that makes it easy for users. The level of usefulness of this system, must be measured whether this system will be useful or not. This research is focused on testing the interface of the Disaster Information System belongs to BNPB Magelang. The method used is Planning and Analysis, Usability Evaluation, Data Analysis and Data Presentation. This measurement is done by the usability testing method. Based on the measurement results of the usability level of the Disaster Information System, a value of 3.35 was obtained. Based on the Linkert Scale, this system is said to be Usable. The results of the research can describe the reusability level of disaster information systems and to recommend in improving aspects of reusability.
Inconsequential Appearances

We compared anthropomorphic language use in online forums about the Amazon Echo Show, Q.bo One, and Anki Vector; carrying out a content analysis of forum and Reddit threads, as well as a Facebook group. Our expectation was to find the highest amount of anthropomorphism for Q.bo One due to its humanoid shape, however, findings suggest that the life-likeness of the artifact is not pre-dominantly linked to the appearance, but to its interactivity and attributed agency and gender.
Open Source Systems Bug Reports

Bug Tracking System (BTS) is a wealthy source of software development information. They contain many insights about the health status of the software project. Making sense of this information is a big challenge to software development communities. In this work, we perform an investigation on fixing time, components, and platforms related to five open source systems hosted by Bugzilla. The motive is to identify what are the most error-prone components and to allocate the right developer to fix the bug. The results are indicators for how data in BTS should be utilized for decision-making processes. The results reveal a strong relationship between bugs and committers, where it is seen that committer is usually related to fixing a single domain of bugs that also shows their expertise. This study can also help in the automated classification of bug allocation to the right kind of committers instead of manual allocation that will result in a reduction of fixing time and interloping between different committers.
Novel Fault Injection Attack without Artificial Trigger

Theoretical process of fault injection attacks is defined as a process of recovering a secret key assuming that an attacker can inject faults into a specific targeted operation. Therefore, an artificial triggering is required to execute such an attack. However, when conducting analysis on real devices, artificial triggering needs to rely on a powerful assumption, such as manipulation of internal codes. In this paper, we propose a novel fault injection system using Input/Output (I/O) signals of target devices as a trigger for relaxing an attacker assumption. This system does not require an implementation of artificial triggering as input signals are used as a trigger in transmission of plaintexts for fault injection attacks. As a result, the attacker can perform fault injection attacks concerning the entire encryption process. To decide the fault injection time based on the trigger, the proposed system applies simple power analysis (SPA), employing electromagnetic emission of target devices. Considering that the fault injection time identified by SPA can be relatively vague compared with that obtained using a system based on an artificial triggering, we address this problem by proposing a process to recover the secret key without knowing the byte index of an injected fault.
Mixing Probabilistic and non-Probabilistic Objectives in Markov Decision Processes

In this paper, we consider algorithms to decide the existence of strategies in MDPs for Boolean combinations of objectives. These objectives are omega-regular properties that need to be enforced either surely, almost surely, existentially, or with non-zero probability. In this setting, relevant strategies are randomized infinite memory strategies: both infinite memory and randomization may be needed to play optimally. We provide algorithms to solve the general case of Boolean combinations and we also investigate relevant subcases. We further report on complexity bounds for these problems.
Discovering Band Order Dependencies

We introduce band ODs to model the semantics of attributes that are monotonically related with small variations without there being an intrinsic violation of semantics. To make band ODs relevant to real-world applications, we make them less strict to hold approximately with some exceptions. Since formulating integrity constraints manually is cumbersome, we study the problem of automatic approximate band OD discovery. We devise an algorithm that determines the optimal solution in polynomial time. We perform a thorough experimental evaluation of our techniques over real-world and synthetic datasets.
Classification of Tajweed Al-Qur'an on Images Applied Varying Normalized Distance Formulas

Al-Qur'an is a Muslim holy book which is written originally in Arabic where the way of reading it should be aligned with the pronunciation and spelling of the messenger and companion in the time of revelation. It is very important to follow the rule of its reading to avoid misinterpretation of the verses, which the existence of artificial neural networks and image processing can be used to classify various type of Tajweed as the reading discipline of Al-Quran in order to support the readers in term of pronunciation and interpretation. In this classification, the recitation of Al-Qur'an in the form of a number of normalized distance formulas in order to obtain the right optimization for the classification namely normalized Manhattan distance which is consistent at a value <0.5 and one that is not applied in the case of Tajweed Al-Qur'an is normalized hamming distance because the value generated is 1.
Potencial De La Educación a Distancia Para Reducir Brechas De Aprendizaje En Educación Superior: Una Mirada Al Caso Colombiano

One of the main needs of Latin America, besides education coverage, is to reduce learning gaps in higher education. In order to respond to such need, many strategies have been designed, worldwide, to impact the demand and provision of higher education programs. Among these strategies we can find grants and scholarships, financial aids, and others. Many have been the options that have been proposed to raise the provision of higher education programs, such as offering technology-supported education modalities like blended-learning, Massive Open Online Courses (MOOCs), and distance and online education. This paper aims to explore the potential of the provision of distance education programs to close learning gaps in Colombia’s higher education. In order to do so, we present an estimatation of the effects that students and programs characteristics can have on the results of the test “Saber PRO” (a Colombian standardized test that measures the level of competences developed by students in different higher education programs). This estimation was done applying a mixed model (Hierarchical Linear Modeling-HLM) based on the information of 451,358 students who were assessed in their last phase of their university major. Data were collected from 2016 to 2018.The econometrics estimations show that controlling individuals’ backgrounds and the characteristics of different academic programs, the variables that were highly associated with the reduction of learning gaps among individuals registered in distance programs compared to face-to-face programs are the students’ age and their cognitive backgrounds (grades in standardized tests taken before their undergraduate studies). The results show that we could expect distance education to become an efficient mechanism to contribute to the reduction of learning gaps in higher education. The quantitative findings suggest a series of research questions that invite to reflect about the potential of distance education in medium-income countries.
Chasing Play with Instagram: How Can We Capture Mundane Play Potentials to Inspire Interaction Design?

Play and playfulness permeate our daily lives and are often the target of interaction designers. Yet, designing for play while embracing the idiosyncrasies of users and their contexts is challenging. Here we address recent calls for new situated and emergent play design methods by turning to social media, which is currently a source of inspiration for arts, crafts, fashion, and more. We present @chasing.play: An exploration of how Instagram may help designers capture and share instances of mundane playful engagement to inspire play design. We report on the findings of a pilot study where we experimented with the tool, and raise a challenges and open questions we plan to address in the future. Our work can trigger discussions among researchers about the potential of social media as a design tool and inspire action towards collectively defining strategies to leverage that potential.
Digital recognition of electric meter with deep learning

Automatic meter reading (AMR) is faster and more efficient than manual meter reading by meter readers. Due to low cost and non-intrusiveness, image-based automatic meter reading attracts attention of researchers. However, existing image-based meter reading is only for specific type of electric meter, which is lack of adaptability. To deal with this problem, a method of meter reading based on deep learning that is applicable to different types of meters is proposed in this work. With the digital area recognition and meter type identification by deep learning and image enhancement, the effect of different appearances of digital screen different shooting angles and environmental interference can be eliminated, thus improving the adaptability of the method. Experimental results show that digital of different types of electric meters can be recognized efficiently by the proposed method.
Conditional Random Field based salient proposal set generation and its application in content aware seam carving

Most of the existing generic object localization algorithms usually give the plausible object locations without taking into consideration the saliency ordering of the proposal set. This paper presents a novel object proposal generation which ranks the key objects according to their saliency score in the proposal pool. First, we formulate a Bayesian framework for generating a probabilistic edgemap which is used to assign a saliency value to the edgelets. A conditional random field is then learnt for edge-labeling by effectively combining the edge features with the relative spatial layout of the edge segments. Lastly, we propose an objectness score for the generated proposal set by analyzing the salient object edge density completely lying within the candidate boxes. Extensive experiments on the benchmark PASCAL VOC 2007 and 2012 datasets demonstrate that the proposed method provides competitive performance against popular generic object detection techniques while using fewer number of proposals. Additionally, we demonstrate the applicability of the generated proposal set for content aware image retargeting.