Reviewerly/exHarmony huggingface dataset:
Improved Scalability of Vehicle Diagnostic Software: Advantages of Service-Oriented Architecture

The increasing number of functions in modern vehicle leads to an exponential increase in software complexity. The validity and reliability of all components must nevertheless be ensured, making the use of appropriate vehicle diagnostics systems indispensable. The demands on the software of those systems have become dynamic and multifaceted. This paper proposes the approach of service-oriented software architecture as an answer to increased complexity and flexibility of a vehicle diagnostic system. In order to offer competitive diagnostic tools, new requirements must be implemented and offered as quickly as possible. Instead of a monolithic software package that is fixed when the tester is delivered, the diagnostic system breaks down complex functionality into “services” that are less restrictively interconnected. It is important to have a forward-looking design that defines the interfaces and leaves room for changes later-on. Strict modularization made it possible to realize a new type of vehicle diagnostic system that can offer versatile tester functions such as interactive vehicle diagnostics or monitoring and recording of vehicle CAN-bus and other communication and reaction to events. These functions are independent and run in parallel, and can thus be optimally adapted to customer requirements.
Service Clustering Based on GSDMM Topic Model

Service clustering is an important part of realizing accurate service discovery and efficient service management. Research on how to improve the accuracy and precision of clustering has become the main task in the field of service clustering. The clustering method proposed in this paper first preprocesses the service description text, extracts feature words that have positive significance for category division, and reduces data sparsity; further, uses the GSDMM topic model to generate the corresponding topic vector; finally, through AGNES algorithm performs the final clustering effect analysis on the vector. Experiments have proved that the GSDMM topic model method proposed in this paper is effective for service clustering.
tfaip - a Generic and Powerful Research Framework for Deep Learning based on Tensorflow

tfaip is a Python-based research framework for developing, structuring, and deploying Deep Learning projects powered by Tensorflow (Abadi et al., 2015) and is intended for scientists of universities or organizations who research, develop, and optionally deploy Deep Learning models.tfaip enables both simple and complex implementation scenarios, such as image classification, object detection, text recognition, natural language processing, or speech recognition.Each scenario is highly configurable by parameters that can directly be modified by the command line or the API.
A Hierarchy-Based System for Recognizing Customer Activity in Retail Environments

Customer activity (CA) in retail environments, which ranges over various shopper situations in store spaces, provides valuable information for store management and marketing planning. Several systems have been proposed for customer activity recognition (CAR) from in-store camera videos, and most of them use machine learning based end-to-end (E2E) CAR models, due to their remarkable performance. Usually, such E2E models are trained for target conditions (i.e., particular CA types in specific store spaces). Accordingly, the existing systems are not malleable to fit the changes in target conditions because they require entire retraining of their specialized E2E models and concurrent use of additional E2E models for new target conditions. This paper proposes a novel CAR system based on a hierarchy that organizes CA types into different levels of abstraction from lowest to highest. The proposed system consists of multiple CAR models, each of which performs CAR tasks that belong to a certain level of the hierarchy on the lower level’s output, and thus conducts CAR for videos through the models level by level. Since these models are separated, this system can deal efficiently with the changes in target conditions by modifying some models individually. Experimental results show the effectiveness of the proposed system in adapting to different target conditions.
Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media

Security agencies face an emerging challenge of identifying and counter the malicious contents spread on the social media by the terrorists.However, text classification techniques are limited by visualization, pre-processing, features extraction, and larger features space.Additionally, change in criminal content require the learning models to identify altered malicious textual contents which poses extra challenge.This study proposes simplified yet adaptable framework that uses a novel features extraction algorithm for extracting features from the textual part of social media contents.The feature extraction considers selective features from only 8 dimensions and follows a six step process.The extracted features are suitably used to train the state vector machine for the classification of the malicious content.The performance of the proposed method is evaluated against other popular feature selection/ extraction algorithms like term frequency-inverse document frequency, Gini Index (GI), Chi square statistics, and PCA.Additionally, machine learning classifiers like decision tree, random forest, and Naïve Bayes are also used for classification.Results suggest that the proposed approach consumes less energy on text visualization, pre-processing, and dimensionality reduction.It also reduces the time-space complexity of the features extraction process and is capable to steer according to the changing strategies of the active criminal groups.In addition, it can effectively analyze the propaganda material published by the extremists.It automatically identifies the radical text on social media platforms allowing understanding of the behaviors, characteristics and subsequent blockage of such content.
Time Series Segmentation Clustering: A New Method for S-Phase Picking in Microseismic Data

Phase picking is a critical step in the analysis of microseismic data. However, there is a lack of relevant methods for S-phase picking. In this letter, we pick the P-phase and extract the data around the S-phase arrival firstly based on previous studies. Then, we propose a method called time series segmentation clustering (TSSC) which is based on the K-means algorithm to pick the S-phase. In the TSSC method, we construct a feature vector of the microseismic signal and standardize the feature vector first. Then, we modify the K-means objective function into a function of time. We subsequently obtain the S-phase arrival corresponding to the minimum value of the objective function. We tested the objective function of the TSSC method on synthetic signals and microseismic signals, and the results show that the method is reliable. This automatic picking algorithm has been used to pick 1763 S-phase arrivals from microseismic signals. In comparison with manual picking results, a statistical analysis shows that this method is feasible.
A new time adjusting control limits chart for concept drift detection

Time varying dynamic systems whose underlying changing distribution should be continuously monitored to track abnormal behaviors are one of the most recent challenges in many practical applications. When the data arrive in a continuous way, the target concept to be monitored may change accordingly causing a problem of concept drift. Thus, distinguishing between true alarms and changes due to the nonstationarity of the loading data is required. Traditional control charts cannot handle such processes since they do not use a change dependent procedure and they are not designed for concept drift problems. This article proposes the first two-stage time adjusting control chart for monitoring data stream processes with concept drift. Stage I updates the control limits each time an adjustment condition is satisfied based on an incremental linear combination of the historical and the new data. Stage II validates the shift detected in Stage I by testing whether the two subsamples around the drift belong to the same distribution. Experiments based on several drift situations and using different performance measures show that the proposed adaptive chart is more robust than the most recent competitive time varying charts existing in the literature. Moreover, it is more efficient in terms of shift recognition by reducing the false negative detections in several cases. • A new time adjusting control limit chart especially designed for monitoring non-stationary processes with concept drift is proposed. • The new method is evaluated in the different change scenarios that may occur in the data. • The proposed adaptive chart has better performance than the only time varying control chart competitor existing in the literature.
Manipulator: A Novel Collusion Attack on Trust Management Systems in Social IoT

Billions of smart devices communicate with each other in the era of IoT. They share data, contribute to make decisions and sense to cooperatively perform business operations. The IoT devices implement social networking paradigms to overcome uncertainty and achieve better results. This enhances Social IoT (SIoT), a relatively new subset of IoT where smart entities form a social network of their own to provide better quality of service. Trust is what holds this social togetherness. There are various trust management protocols for managing trust between IoT entities. They were verified with known trust-related attacks such as self-promoting, bad-mouthing, ballot-stuffing etc. In this work a new trust-related attack called manipulator is introduced. It is based on historical trust consideration mechanism in trust management systems established as a safeguard for known attacks. Manipulator attack takes advantage of systems proactive interception on lack of trust and transform it to a friendly fire by deceiving that non-malicious entities were also in collusion all along. The vulnerable trust management schemes in literature against manipulator attack were discussed as well.
Comparisons Between First Person Point-of-View 180° Video Virtual Reality Head-Mounted Display and 3D Video Computer Display in Teaching Undergraduate Neuroscience Students Stereotaxic Surgeries

Introduction: Students interested in neuroscience surgical applications learn about stereotaxic surgery mostly through textbooks that introduce the concepts but lack sufficient details to provide students with applied learning skills related to biomedical research. The present study employed a novel pedagogical approach which used an immersive virtual reality (VR) alternative to teach students stereotaxic surgery procedures through the point of view (POV) of the neuroscientist conducting the research procedures. Methods: The study compared the 180° video virtual reality head-mounted display (180° video VR HMD) and the 3D video computer display groups to address the learning gaps created by textbooks that insufficiently teach stereotaxic surgery, by bringing students into the Revinax ® Virtual Training Solutions educational instruction platform/technology. Following the VR experience, students were surveyed to determine their ratings of the learning content and comprehension of the material and how it compared to a traditional lecture, an online/hybrid lecture, and YouTube/other video content, as well as whether they would have interest in such a pedagogical tool. Results: The 180° video VR HMD and the 3D video computer display groups helped students attend to and learn the material equally, it improved their self-study, and they would recommend that their college/university invest in this type of pedagogy. Students reported that both interventions increased their rate of learning, their retention of the material, and its translatability. Students equally preferred both interventions over traditional lectures, online/hybrid courses, textbooks, and YouTube/other video content to learn stereotaxic surgery. Conclusion: Students preferred to learn in and achieve greater learning outcomes from both the 180° video VR HMD and the 3D video computer display over other pedagogical instructional formats and thought that it would be a more humane alternative to show how to conduct the stereotaxic surgical procedure without having to unnecessarily use/practice and/or demonstrate on an animal. Thus, this pedagogical approach facilitated their learning in a manner that was consistent with the 3-Rs in animal research and ethics. The 180° video VR HMD and the 3D video computer display can be a low-cost and effective pedagogical option for distance/remote learning content for students as we get through the COVID-19 pandemic or for future alternative online/hybrid classroom instruction to develop skills/reskill/upskill in relation to neuroscience techniques.
A Parallel Processing Technique for Extracting and Storing User Specified Data

Users are often interested in a specific type of data (preferences) available from a large volume of data collected on the system. An efficient and effective system that can only store the user preferred data from the large raw data set helps the users to search for relevant information on time. The motivation behind this paper is to devise such a technique. The technique uses machine learning as well as parallel processing to efficiently filter out the user preferred data from a large raw data set. Firstly, the technique stores the filtered data and discards the remaining data which saves storage space for the user. Secondly, it leads to an enhanced searching experience for the user by reducing the search latency. Running the filtering operation can be CPU intensive which often leads to high latency for extracting user preferred data from the raw data set. To solve this problem, the technique employs parallel processing and machine learning, thus reducing the data filtering latency while making the data searching process faster for the user. A proof-of-concept prototype for this technique has been built on the Apache Spark parallel processing engine. The prototype is subjected to several performance experiments using synthetic datasets. The analysis of experimental results shows the viability of the proposed technique and provides insights into system behavior and performance.
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes

Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. However, existing GP approximations do not simultaneously support large numbers of off-the-grid spatial data-points and long time-series which is a hallmark of many applications. Pseudo-point approximations, one of the gold-standard methods for scaling GPs to large data sets, are well suited for handling off-the-grid spatial data. However, they cannot handle long temporal observation horizons effectively reverting to cubic computational scaling in the time dimension. State space GP approximations are well suited to handling temporal data, if the temporal GP prior admits a Markov form, leading to linear complexity in the number of temporal observations, but have a cubic spatial cost and cannot handle off-the-grid spatial data. In this work we show that there is a simple and elegant way to combine pseudo-point methods with the state space GP approximation framework to get the best of both worlds. The approach hinges on a surprising conditional independence property which applies to space--time separable GPs. We demonstrate empirically that the combined approach is more scalable and applicable to a greater range of spatio-temporal problems than either method on its own.
Understanding and predicting user dissatisfaction in a neural generative chatbot

Neural generative dialogue agents have shown an increasing ability to hold short chitchat conversations, when evaluated by crowdworkers in controlled settings. However, their performance in real-life deployment – talking to intrinsically-motivated users in noisy environments – is less well-explored. In this paper, we perform a detailed case study of a neural generative model deployed as part of Chirpy Cardinal, an Alexa Prize socialbot. We find that unclear user utterances are a major source of generative errors such as ignoring, hallucination, unclearness and repetition. However, even in unambiguous contexts the model frequently makes reasoning errors. Though users express dissatisfaction in correlation with these errors, certain dissatisfaction types (such as offensiveness and privacy objections) depend on additional factors – such as the user’s personal attitudes, and prior unaddressed dissatisfaction in the conversation. Finally, we show that dissatisfied user utterances can be used as a semi-supervised learning signal to improve the dialogue system. We train a model to predict next-turn dissatisfaction, and show through human evaluation that as a ranking function, it selects higher-quality neural-generated utterances.
Hardware-Software Codesign of Weight Reshaping and Systolic Array Multiplexing for Efficient CNNs

The last decade has witnessed the breakthrough of deep neural networks (DNNs) in various fields, e.g., image/speech recognition. With the increasing depth of DNNs, the number of multiply-accumulate operations (MAC) with weights explodes significantly, preventing their applications in resource-constrained platforms. The existing weight pruning method is considered to be an effective method to compress neural networks for acceleration. However, weights after pruning usually exhibit irregular patterns. Implementing MAC operations with such irregular weight patterns on hardware platforms with regular designs, e.g., GPUs and systolic arrays, might result in an underutilization of hardware resources. To utilize the hardware resource efficiently, in this paper, we propose a hardware-software codesign framework for acceleration on systolic arrays. First, weights after unstructured pruning are reorganized into a dense cluster. Second, various blocks are selected to cover the cluster seamlessly. To support the concurrent computations of such blocks on systolic arrays, a multiplexing technique and the corresponding systolic architecture is developed for various CNNs. The experimental results demonstrate that the performance of CNN inferences can be improved significantly without accuracy loss.
TREE-BASED SEMANTIC ANALYSIS METHOD FOR NATURAL LANGUAGE PHRASE TO FORMAL QUERY CONVERSION

Context. This work is devoted to the problem of natural language interface construction for ontological graph databases. The focus here is on the methods for the conversion of natural language phrases into formal queries in SPARQL and CYPHER query languages. Objective. The goals of the work are the creation of a semantic analysis method for the input natural language phrases semantic type determination and obtaining meaningful entities from them for query template variables initialization, construction of flexible query templates for the types, development of program implementation of the proposed technique. Method. A tree-based method was developed for semantic determination of a user’s phrase type and obtaining a set of terms from it to put them into certain places of the most suiting formal query template. The proposed technique solves the tasks of the phrase type determination (and this is the criterion of the formal query template selection) and obtaining meaningful terms, which are to initialize variables of the chosen template. In the current work only interrogative and incentive user’s phrases are considered i.e. ones that clearly propose the system to answer or to do something. It is assumed that the considered dialog or reference system uses a graph ontological database, which directly impacts the formal query patterns – the resulting queries are destined to be in SPARQL or Cypher query languages. The semantic analysis examples considered in this work are aimed primarily at inflective languages, especially, Ukrainian and Russian, but the basic principles could be suitable to most of the other languages. Results. The developed method of natural language phrase to a formal query in SPARQL and CYPHER conversion has been implemented in software for Ukrainian and Norwegian languages using narrow subjected ontologies and tested against formal performance criteria. Conclusions. The proposed method allows the dialog system fast and with minimum number of steps to select the most suitable query template and extract informative entities from a natural language phrase given the huge phrase variability in inflective languages. Carried out experiments have shown high precision and reliability of the constructed system and its potential for practical usage and further development.
“Try, Try, Try Again:” Sequence Analysis of User Interaction Data with a Voice User Interface

Voice User Interfaces (VUIs) pose challenges for users to learn the system’s supported features and commands. Users often rely on trial-and-error to navigate VUI dialogues and complete desired tasks. The order in which users try different commands contains vital information about how they learn. In this paper, we explore using sequence analysis techniques to reveal the patterns of tactics our participants (n = 50) used when interacting with an unfamiliar multi-modal VUI. We present the results of our sequence analysis, the top sequences used, and a cluster analysis of our participants on their usage of the top sequences. Our results indicate participants initially struggled with understanding the acceptable utterance structure and entities more so than utterance keywords. Additionally, we discuss our participants’ behavior differences and discuss the usefulness of this methodology of sequence analysis for HCI VUI research.
Cognitive Load of Ontology as a Means of Information Representation in the Educational Process

The article describes the process of assessing the cognitive load of ontology as a means of information representation in the educational process. Such a process allows evaluating the visual displaying of the ontology from the point of view of its cognitive perception. The characteristics of information cognitive perception are presented, as well as the important features of the cognitive load in the context of the educational process are identified. The basic means of information representation used in the educational process is considered. The approach to knowledge mapping is described, in which knowledge maps act as the basic principle of information visualization to facilitate its perception. It is determined that ontology, acting as a domain of a conceptual model, provides the formation of a knowledge base for the information displayed using a knowledge map representation and storage. A review of studies on the use of ontologies in the educational process is given. The elements of ontology and software tools for its development are described, as well as the main levels of information representation stored in the ontology are highlighted. It was revealed that among a variety of means for ontology displaying ways, it is the graphic method that allows implementing a visual representation of the ontology concepts (and thereby the terms of subject domain and the relationships between them), which simplifies the cognitive perception of the information contained in the ontology. Various metrics for assessing the quality of the developed ontology are outlined. Among them, a group of cognitive ergonomics metrics is distinguished that allows assessing the ontological model in terms of its visual representation quality.