Reviewerly/exHarmony huggingface dataset:
Efficient Multi-Robot Coverage of an Unknown Environment

This paper discusses the complete area coverage of multi-robots in unknown environments. Finding the optimal solution for the problem of area coverage with multiple robots has been proven to be NP-hard. This paper proposes the Multi-agent Node Backtracking Area Coverage (MANBAC) algorithm, which uses improved BCD local area coverage method, and the node backtracking method that is based on the A* algorithm to achieve complete coverage of the entire unknown area. Compared with the area coverage of a single robot, use multi robots can greatly improve the efficiency of area coverage. Compared with the existing coverage algorithms in known area, this study can be applied to a wider range of area scenarios which are not known.
Arangopipe, a tool for machine learning meta-data management

Experimenting with different models, documenting results and findings, and repeating these tasks are day-to-day activities for machine learning engineers and data scientists. There is a need to keep control of the machine-learning pipeline and its metadata. This allows users to iterate quickly through experiments and retrieve key findings and observations from historical activity. This is the need that Arangopipe serves. Arangopipe is an open-source tool that provides a data model that captures the essential components of any machine learning life cycle. Arangopipe provides an application programming interface that permits machine-learning engineers to record the details of the salient steps in building their machine learning models. The components of the data model and an overview of the application programming interface is provided. Illustrative examples of basic and advanced machine learning workflows are provided. Arangopipe is not only useful for users involved in developing machine learning models but also useful for users deploying and maintaining them.
Expanding Designing for One to Invite Others Through Reverse Inclusion

This research aims to explore how tangible technology created through co-design can be designed in a way that invites social interaction for people with intellectual disability. We conducted co-design sessions with one participant to create a sensory musical blanket. As the trials were run in a collective environment, their peers were drawn to the developing design. The design method and unique interactions are key contributions of this research.
Path Optimization Development of Sports Industry Management Development under the Background of Artificial Intelligence Era

In recent years, the state has attached great importance to industrial management and the development of the sports industry, and has continuously promulgated and implemented industrial management and sports industry development policies and planning documents. Sports industry management is a sunrise industry and a new industry in the 21st century. As an important part of industry management, it is inseparable from the development of the sports industry. At the same time, as the basic administrative division of our country, the county is in a key position as a link between the past and the next. The purpose of this article is to study the path optimization development of sports industry management development under the background of artificial intelligence era. This research comprehensively adopts the method of literature data, expert interview method, questionnaire survey method and decision analysis method. Combined with the national and provincial and municipal sports industry and industrial management development plans, as well as the current artificial intelligence technology, a macroscopic construction of the sports industry management development path is carried out from the theoretical level. Under the original management development of the sports industry, the use of artificial intelligence technology has increased the overall development rate by 20%.
Optimizing Multi-Taper Features for Deep Speaker Verification

Multi-taper estimators provide low-variance power spectrum estimates that can be used in place of the windowed discrete Fourier transform (DFT) to extract speech features such as mel-frequency cepstral coefficients (MFCCs). Even if past work has reported promising automatic speaker verification (ASV) results with Gaussian mixture model-based classifiers, the performance of multi-taper MFCCs with deep ASV systems remains an open question. Instead of a static-taper design, we propose to optimize the multi-taper estimator jointly with a deep neural network trained for ASV tasks. With a maximum improvement on the SITW corpus of 25.8% in terms of equal error rate over the static-taper, our method helps preserve a balanced level of leakage and variance, providing more robustness.
A Low-Cost Hardware Design of Learning-Based One-Dimensional Interpolation for Real-Time Video Applications at the Edge

Gradual advances have occurred in the design of micro-artificial intelligence for resource-limited hardware. A high-resolution (HR) image reconstruction module is indispensable for video analytics chips or devices at the edge. This paper proposes a low-cost and learning-based interpolation method for HR image reconstruction. The proposed method generates reconstructed pixels by processing reference pixels with optimal weights, which are pre-trained by solving the minimum mean square error problem for real images. To reduce the number of computation units and the usage of learned weights, a cross-directional interpolation architecture, which includes a vertical kernel and a horizontal kernel, is adopted. Moreover, a one-dimensional feature discriminator is proposed to improve the quality of up-scaled images efficiently. The main benefit of the proposed method is that it requires a small number of computation units but can still produce high-quality images. The hardware architecture of the proposed method was implemented on a field-programmable gate array (FPGA) by using Xilinx UltraScale+ ZCU102 and an application-specific integrated circuit (ASIC) by using TSMC’s 0.13- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> technology. On the ASIC, the proposed hardware required only approximately 60K gate counts and 50 KBytes of memory. The experimental results indicate that the average peak signal-to-noise ratio of the up-scaled images reached 35.92 dB for the Set-5 dataset. The throughput of the proposed hardware was at least 1000 Mpixels/s on the FPGA and 1200 Mpixels/s on the ASIC, which indicates that the proposed hardware can handle a target resolution higher than 4K in real time.
Abnormal Occupancy Grid Map Recognition using Attention Network

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural network with novel attention mechanism modules. We propose an effective channel and spatial Residual Squeeze-and-Excitation (csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiments with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested a few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96.23% for abnormal occupancy grid map recognition.
Strategies for the Utilization of Virtual Reality Technologies in the First Year of Architectural Education

The first year of architectural education is a crucial period in which students get introduced to the ambiguous nature of architecture and build the foundations of their professional career. It has a dense structure; theoretical and technical courses gathered around the design studio which is the core of architectural education. Its dynamic and flexible nature makes architectural education open for innovations and the implementation of emerging technologies. Accordingly, digital technologies that have a strong relationship with the profession of architecture, also have firm effects on architectural education. Even though it is not common among architectural education institutions around the globe, emerging digital technologies may have a role in the first year of architectural curriculum. One of the digital technologies that can be utilized in the first year of architectural education in virtual reality technologies as they provide an additional medium for experiencing architectural products and alternative methods for designing them.This paper investigates the necessity and potential benefits of VR technologies in the first year of architectural education. Based on a case study conducted among freshman students, the VR technologies prove themselves useful. The paper also presents various methods and strategies, and their potential benefits for the implementation of VR technologies into the different domains of first year architectural curriculum.
Research on Educational Information Management System Based on Computer Big Data

Abstract Aiming at the problem of computer network courses with more teaching content and rapid knowledge update, the paper builds a computer network adaptive learning system by collecting learning behaviour data in the education process and using the learning analysis technology of big data. The six modules of prediction and intervention can push learning content in a targeted manner, give feedback on learners’ learning effects in time, and recommend next learning strategies, so as to achieve the effect of teaching students in accordance with their aptitude and cultivating students’ independent learning ability.
Negotiating Repairedness: How Artifacts Under Repair Become Contingently Stabilized

This paper examines "repairedness" - the contingently stable, working version of an artifact under repair that is negotiated out of multiple possible versions to bring about the temporary conclusion of repair work. Our paper draws on an ethnographic study of an analog electronics repair community in Seoul, South Korea to develop two contributions. First, studying processes of negotiating the repairedness of an artifact accounts for contingency in the properties of the artifact itself, which differs from contingencies in collaborative work practices that have been a focus of CSCW research on repair. Second, a concept of repairedness highlights how ongoing processes of interacting with an artifact nonetheless need to be brought to contingent conclusions, suggesting that an artifact's properties are a valuable site for sustainable engagement. These contributions help CSCW research on repair account for the multiplicity of artifacts highlighted by STS scholars as integral to how humans sustainably engage with artifacts in their practices.
Extending Interaction to Human-Computer Integration

The main goal of this paper is to characterize the concept and emerging paradigm of Human-Computer Integration (HInt) in terms of its definition, its relationship with HCI, as well as its opportunities and challenges for HCI. To achieve this goal, we conducted a Systematic Literature Review. As contribution, this paper helps the HCI community: 1) to expand its knowledge in relation to this new concept/paradigm and 2) to direct its efforts and contributions towards the consolidation and evolution of Human-Computer Integration (Hint) as a new HCI paradigm.
Cloud Computing Management Architecture for Digital Health Remote Patient Monitoring

With machine learning, the remote patient monitoring (RPM) devices are no longer just remote data collection devices. In addition to data analytics, data security and systems integration are also core challenges for developers of the next generation of innovative RPM devices. This includes overcoming technological barriers on applying machine learning algorithms to patient data directly on devices and regulatory barriers on patient data privacy. To address these challenges, this study proposed a unified edge-cloud computing architecture to effectively integrate all the RPM devices in use by the individual patient. All the remote patient monitoring data are managed by edge computing, only the latent representations are uploaded to the cloud for AI-assisted decision making. The proposed model has three modules. The edge medical image module used a subspace learning model for anomalies detection and unhealthy signs and symptoms classification. The edge medical time series module used spectral residual for anomalies detection and scattering wavelet network for severity classification. The cloud telehealth management module used convolutional neural network, recurrent neural network and attention model to provide individual patient treatment plan and medicine delivery schedule. The proposed platform has been tested on various RPM devices to provide AI-based anomaly detection and symptoms classifications. The application of the proposed platform has demonstrated that the on-device training model can enable faster and more accurate diagnosis and treatment. For meso-level organizational interoperability on health information exchange, we will only transmit the latent representation instead of the patient’s raw data to reduce cyberattacks and ensure confidentiality of health data.
PeaGlyph: Glyph design for investigation of balanced data structures

For many data analysis tasks, such as the formation of well-balanced groups for a fair race or collaboration in learning settings, the balancing between data attributes is at least as important as the actual values of items. At the same time, comparison of values is implicitly desired for these tasks. Even with statistical methods available to measure the level of balance, human judgment, and domain expertise plays an important role in judging the level of balance, and whether the level of unbalance is acceptable or not. Accordingly, there is a need for techniques that improve decision-making in the context of group formation that can be used as a visual complement to statistical analysis. This paper introduces a novel glyph-based visualization, PeaGlyph, which aims to support the understanding of balanced and unbalanced data structures, for instance by using a frequency format through countable marks and salient shape characteristics. The glyph was designed particularly for tasks of relevance for investigation of properties of balanced and unbalanced groups, such as looking-up and comparing values. Glyph-based visualization methods provide flexible and useful abstractions for exploring and analyzing multivariate data sets. The PeaGlyph design was based on an initial study that compared four glyph visualization methods in a joint study, including two base glyphs and their variations. The performance of the novel PeaGlyph was then compared to the best “performers” of the first study through evaluation. The initial results from the study are encouraging, and the proposed design may be a good alternative to the traditional glyphs for depicting multivariate data and allowing viewers to form an intuitive impression as to how balanced or unbalanced a set of objects are. Furthermore, a set of design considerations is discussed in context of the design of the glyphs.
Fast parallel construction of variable-length Markov chains

Alignment-free methods are a popular approach for comparing biological sequences, including complete genomes. The methods range from probability distributions of sequence composition to first and higher-order Markov chains, where a k-th order Markov chain over DNA has [Formula: see text] formal parameters. To circumvent this exponential growth in parameters, variable-length Markov chains (VLMCs) have gained popularity for applications in molecular biology and other areas. VLMCs adapt the depth depending on sequence context and thus curtail excesses in the number of parameters. The scarcity of available fast, or even parallel software tools, prompted the development of a parallel implementation using lazy suffix trees and a hash-based alternative.An extensive evaluation was performed on genomes ranging from 12Mbp to 22Gbp. Relevant learning parameters were chosen guided by the Bayesian Information Criterion (BIC) to avoid over-fitting. Our implementation greatly improves upon the state-of-the-art even in serial execution. It exhibits very good parallel scaling with speed-ups for long sequences close to the optimum indicated by Amdahl's law of 3 for 4 threads and about 6 for 16 threads, respectively.Our parallel implementation released as open-source under the GPLv3 license provides a practically useful alternative to the state-of-the-art which allows the construction of VLMCs even for very large genomes significantly faster than previously possible. Additionally, our parameter selection based on BIC gives guidance to end-users comparing genomes.
Analytical Model for Classifying Areas of Interest in Interactive Systems

Several organizations have invested in solutions to improve user experience when interacting with computer systems and tools. One of the applied strategies is the study of users behavior in order to identify which areas of interest (AOI) mostly attract their attention in the application interfaces. In this context, this paper proposes an analytical model that defines AOI based on HyperText Markup Language (HTML) objects of the system interface and classifies them based on an algorithm that uses data from the Tracking Techniques Based User Experience Evaluation Tool (T2-UXT). To evaluate the proposal, a feasibility study was carried out on the Amazon portal. Results allow us to visualize a ranking of areas that mostly attract attention from users during the interaction. As such, it can assist organizations in the creation of interacting ways that benefit their interests as well as their customers' interests.
How Can You Resolve a Trilemma? - A Topological Approach -

This paper discusses how to escape a state in which argumentation can reach no conclusion, by offering a new argument. We formalize our approach based on Dung’s abstract argumentation framework (AF). When an AF has no stable extension, we have no meaningful conclusion. We address the problem of whether it is possible to revise this situation by adding an argument that attacks an existing one. If possible, how many solutions can we generate and at what position should it be added? We discuss this problem using an AF consisting of a trilemma and show conditions depending on the topology of the AF. We also address the point that a specific argument can be accepted or not by this action. We extend the discussion into two possible directions: a general N-lemma case and a set of AFs, each of which consists of several trilemmas. It follows that when a large argumentation becomes stuck in a practical situation, the position to which a counter-argument should be added can be detected by a check of the topology of the AF.
“Better Communication Leads to a Higher Output?” An Analysis of Pair Communication on Pair Programming Productivity

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> This study focuses on how group communication affects group productivity. The specific scope of the study is pair programming. The study aims to discover whether intra-pair communication in pair programming has a significant impact on the pair programming process and output. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> Many of the pair programming communication studies are descriptive and qualitative studies whose foci lay more on communication contents and alternative message deliveries. As a result, more research that focuses on analyzing the effectiveness of a person's communication skill level while performing a demanding task is needed. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research question:</b> Does the communication competency level significantly impact pair programming output? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</b> A pool of novice university programming students was deployed for the experiment. The Conversational Skills Rating Scale (CSRS) was used to categorize them into three cohorts—"high-high,” “high-low,” and “low-low.” The confounding variables were controlled. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> No significant difference was found among the three cohorts in terms of their pair programming code output. Additionally, the post-experiment questionnaire responses revealed no significant difference in compatibility and confidence levels, but did show a significant difference in communication level. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</b> With all things being equal, a programmer's high communication skill level doesn't play a significant role in the programming output in a pair programming setting.
Augmenting ASR for User-Generated Videos with Semi-supervised Training and Acoustic Model Adaptation for Spoken Content Retrieval

We present an investigation into the use of semi-supervised training and content genre adaptation for improved automatic speech recognition (ASR) of diverse user-generated videos in the task of spoken content retrieval (SCR). Previous work has successfully applied semi-supervised training in single domain ASR tasks. Our focus is on the exploration of the effective use of semi-supervised training of ASR systems for transcription of the spoken content stream of user-generated video data in varied domains and acoustic noise conditions for use in SCR systems. We examine all elements of ASR system development including: data segmentation, data selection, genre labels, acoustic modelling and language modelling using semi-supervised training. We evaluate its effectiveness for ASR and a known-item SCR task using the Blip100000 multimedia collection. Our baseline hybrid ASR system trained out-of-domain produced WERs 31.27% and 44.69% on dev and test sets, respectively. By introducing the techniques outlined above, the WERs are reduced to 26.82% and 39.21% respectively. The improved transcripts increased mean reciprocal rank (MRR) results for the SCR task from 15.59% to 39.38% on dev and 20.98% to 37.23% on test sets.