Reviewerly/exHarmony huggingface dataset:
Hardware Acceleration Implementation of Three-Dimensional Convolutional Neural Network on Vector Digital Signal Processors

Convolutional neural networks (CNNs) have achieved great success in the field of computer vision. Researchers are currently focusing on more complicated three-dimensional (3D) CNNs and applying them to video processing. However, 3D CNNs have a huge number of calculations and parameters, which must be operated on an efficient computing platform. Digital signal processors (DSPs) are widely used in mobile terminals, high-performance workstations, and supercomputers. As a programmable computing platform, the DSP contains numerous multiplier-accumulator units (MACs) and supports vectorized calculation methods, which is very conducive to the efficient implementation of CNNs. To this end, it is of great significance to study the implementation of CNNs on a DSP. However, the current research on CNN acceleration on a DSP is still insufficient. This paper uses the FT-Matrix as the target DSP platform, which is a vector single instruction/multiple data (SIMD) architecture, and proposes a complete 3D CNN vectorized mapping method on the FT-Matrix, a block access scheme, and a hybrid convolutional layer optimization mapping method based on a sliding window and 3D Winograd algorithm. Our method can also be extended to other DSP platforms. Experimental results show that the algorithm can be executed accurately and efficiently on the DSP.
Research of Interactive Gesture Usability of Navigation Application Based on Intuitive Interaction

With the development of gesture recognition technology, gesture control will be more and more widely used in every scene of life. As for map applications or applications, which people need to use more and more in daily travel, the operation mode of it still needs to be improved. Applying gesture control technology to map applications will provide a better user experience than the original operation mode in a specific scene. Actually, there are many gestures that may not conform to the users’ intuition. With the maturity of non-contact gesture interaction technology, the daily operation of map by gesture will have more advantages than the original operation mode. In this study, users’ intuitive gestures for different map functions are extracted through experiments. And summarized the gestures that were used the most times or the most intuitive gestures corresponding to each function. Finally form a set of intuitive gestures suitable for map applications. The research also analyzes the relationship between users’ intuitive gestures and map tasks. Also analyzes the relationship between users’ intuitive gestures and their previous experience of using intelligent devices, to discuss the impact of the using experience of intelligent devices on users’ intuitive gestures. We conducted gesture research experiments with participants, and we learned about the gestures they chose when facing different tasks and why they chose them. We found similarity between a part of intuitive gestures in experiment and the daily gestures during using intelligent devices.
A Conversational Agent as Facilitator: Guiding Groups Through Collaboration Processes

Till now, conversational agents (CA) are often utilized as assistants to answer FAQs, but can they also be involved in other tasks with humans such as guiding them through a process as a group? We cooperate with an editorial office of an online journal and conduct an action design research project to instantiate a CA to facilitate their meetings. We design and develop the digital collaboration process (CP) of the meeting and a CA facilitator based on IBM Watson to integrate it in the digital environment to execute the CP. We conduct a pilot study and expert interviews to evaluate the CP and the derived design principles to facilitate the group during it and through its thinkLets. Our results show positive perception of the CA’s capability as a facilitator for a group within its limitations and its neutrality during group discussions perceived as one strengths compared to human facilitators.
Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness

Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution.The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA.Various mutation operators exist to solve hard combinatorial problems such as the TSP.In this paper, we propose a hybrid mutation operator called "IRGIBNNM", this mutation is a combination of two existing mutations; a knowledgebased mutation, and a random-based mutation.We also improve the existing "select best mutation" strategy using the proposed mutation.We conducted several experiments on twelve benchmark Symmetric traveling salesman problem (STSP) instances.The results of our experiments show the efficiency of the proposed mutation, particularly when we use it with some other mutations.
Learning Traces, Measurement and Assessment Templates for AIS Interoperability

The current paper contains elements relevant for the conceptual modelling and interoperability of measurements and assessments across adaptive instructional systems (AIS). After an introduction, the first section presents a generic use case, where knowledge acquisition is supported by a sequence of training simulators of increasing complexity, and the need to capture regularity and variations in measurements and assessments across instructional systems. The second section briefly discusses the role of measurements and assessments as a core of functions of adaptive instructional systems. The section indicates that an adaptive instructional system needs minimally to capture references to learners’ performance, knowledge components, learning tasks, and learning attempts. The third section maps the main Generalized Intelligent Framework for Tutoring (GIFT) components to a generic feedback control system model. The mapping makes explicit the dual interpretation of measurements and assessments as both results (learning traces) and functions (computation templates). The fourth section examines some non-proprietary frameworks in terms of their capability to support the interoperability of measurements and assessments across adaptive instructional systems. The section briefly discusses xAPI, the Competency And Skill System (CASS), the Evidence Trace File (ETF), the Training Objective Package (TOP), and the Human Performance Markup Language (HPML). The main contributions of the paper are: 1) an AIS conceptual model based on a feedback control system model, and 2) a brief review of learning data frameworks as they relate to measurements and assessments as both learning traces and computation templates.
An axiomatic framework for influence diagram computation with partially ordered preferences

This paper presents an axiomatic framework for influence diagram computation, which allows reasoning with partially ordered values of utility. We show how an algorithm based on sequential variable elimination can be used to compute the set of maximal values of expected utility (up to an equivalence relation). Formalisms subsumed by the framework include decision making under uncertainty based on multi-objective utility, or on interval-valued utilities, as well as a more qualitative decision theory based on order of magnitude probabilities and utilities. Consequently, we also introduce the order of magnitude influence diagram to model and solve partially specified sequential decision problems when only qualitative (or imprecise) information is available.
Accurate and Efficient Quantized Reservoir Computing System

Quantization is a widely used technique to deploy deep learning models on embedded systems since this technique could reduce the model size and computation dramatically. Many quantization approaches have been proposed in recent years. Some quantization approaches are aggressive which could sufficiently reduce the model size and computation. However, the accuracy could be significantly decreased. To resolve this issue, some research groups have proposed smoother approaches to reduce accuracy loss. However, smoother approaches would use much more resources than aggressive approaches. In our work, we proposed a quantization approach which reduces resource utilization dramatically without losing much accuracy. We have successfully applied our quantization approach to the reservoir computing (RC) system. Compared to the RC system using floating-point numbers, our proposed RC system reduces the resource utilization of BRAM, DSP, Flip-Flop (FF) and Lookup Table (LUT) by 47%, 93%, 93%, and 87%, respectively, while only loses 0.08% accuracy on the NARMA10 dataset. Meanwhile, our proposed RC system uses approximately 45%, 14%, and 21% less BRAM, FF, and LUT respectively than the quantized RC system using other popular quantization approach.
Negative emotion diffusion and intervention countermeasures of social networks based on deep learning

Aiming at the limitation of using only word features in traditional deep learning sentiment classification, this paper combines topic features with deep learning models to build a topic-fused deep learning sentiment classification model. The model can fuse topic features to obtain high-quality high-level text features. Experiments show that in binary sentiment classification, the highest classification accuracy of the model can reach more than 90%, which is higher than that of commonly used deep learning models. This paper focuses on the combination of deep neural networks and emerging text processing technologies, and improves and perfects them from two aspects of model architecture and training methods, and designs an efficient deep network sentiment analysis model. A CNN (Convolutional Neural Network) model based on polymorphism is proposed. The model constructs the CNN input matrix by combining the word vector information of the text, the emotion information of the words, and the position information of the words, and adjusts the importance of different feature information in the training process by means of weight control. The multi-objective sample data set is used to verify the effectiveness of the proposed model in the sentiment analysis task of related objects from the classification effect and training performance.
Suspect Face Generation

Currently sketch artists are employed by the police to draw sketches of suspects based on the description given by an eye-witness. These sketches can sometimes be inaccurate due to incorrect drawings of the artist or the incorrect description given by the witness. Generative Adversarial Network (GAN) is a way of training a Neural Network to output images which belong to a specific class. This network is trained by using an adversarial process which pits the generator against the discriminator in a minimax game. Traditional GANs are unable to generate high-resolution images hence, StyleGAN is used to resolve this issue. The generated images may still need to be altered to get a close match so TL-GAN is used to alter the generated image by altering the latent-space input of the StyleGAN. TL-GAN offers users the ability to finely tune one or multiple features of the face holistically. The main objective of the proposed work is to develop a Suspect Face Generation System as the sketches made by sketch artists are only 13 out of 160 times (approx. 8%) accurate. This system will help the society in reduction of misidentification of crime suspects and considerably reduce the crime rate.
The Framing Effect of Questions in Community Question-Answering Sites

In Community Question-Answering (CQA) sites, the question is often the starting point for information exchange, eliciting a series of responses that result in a response network (RN) whose nodes are the postings, while edges represent the responses between postings. This argument is supported by framing theory which posits that different message forms, their “frames”, lead to different user responses. This research investigated how question frames influenced their resulting RNs. Question frames were operationalized as conversational and informational frames. Social network analysis was conducted to explore the RNs of different question frames for both Science, Technology, Engineering and Mathematics (STEM) and non-STEM topics. Interestingly, most STEM conversational questions had postings that directly responded to the question, whereas informational questions attracted more comments on response postings. However, no significant differences were found in the non-STEM topics. Our research suggests that users should appropriately frame questions to generate desired responses from the community.
Co-designing Strategies to Provide Telecare Through an Intelligent Assistant for Caregivers of Elderly Individuals

Worldwide, the number of people who are over 60 is growing more than any other age group. In the scenario of this research, a mediumsized city in Brazil, elderlies with Chronic Noncommunicable Diseases can have home care services. However, the healthcare professionals who assist these patients regularly face challenges in the visits, such as persistent fear of violence in certain areas. This research proposes the use of Intelligent Conversational Agents as a tool for supporting Telecare practices and tame these challenges. Our literature review, however, has demonstrated that previous works did not follow a methodology to promote a co-design of these agents in a scenario such as ours. There is also a lack of works that balance technological and social issues with non-technological experts in the design process (conceptualization, dialogues elaboration, intelligent agent goals and evaluation). We, thus, have performed a study following a qualitative methodology with the participation of health professionals. The first step aimed to raise the context, dialogues, and objectives of the agent. The second step, using observational and interviewing in a controlled environment, aimed to determine and evaluate the design goals, usefulness, compatibility with the reality, and find out additional requirements to aid the development of an Intelligent Conversational Agent. Results have led us to propose the agent MarIANA (Maria Intelligent conversAtioNal Agent). Therefore, it will act to prevent the clinical complications of these older adults suffering from Chronic Noncommunicable Diseases pointed as a critical matter in a recent work conducted in the city of the scenario of this research.
Analyzing Software Complexities by Applying Data Structure Metrics on Different Programming Languages

A software engineer has to confront many challenges while developing a software product. These challenges increase the degree of complexity of software products. There are a lot of measures available to compute and predict complexity at different phases of software development. Data structure metrics is such a measuring method in which the emphasis is not just on LOC, function points, independent paths, etc. In this measuring technique; the amount of data input, processing it and the output form software is considered to calculate the complexity of the product or sub-system. In this paper, Data Structure metrics of the program for checking for palindrome number in five different programming languages have been taken and analysis has been presented.
Space efficient representations of finite groups

The Cayley table representation of a group uses O(n2) words for a group of order n and answers multiplication queries in time O(1). It is interesting to ask if there is a o(n2) space representation of groups that still has O(1) query-time. We show that for any δ, 1/log⁡n≤δ≤1, there is an O(n1+δ/δ) space representation for groups of order n with O(1/δ) query-time. We also show that for Dedekind groups, simple groups and several group classes defined in terms of semidirect product, there are O(n) space representations with logarithmic query-time where n is the size of the group. Farzan and Munro (ISSAC'06) defined a model for group representation and gave a succinct data structure for abelian groups with constant query-time. They asked if their result can be extended to categorically larger group classes. We construct data structures in their model for Hamiltonian groups and some other classes of groups with constant query-time.
Vectorized Kernel-Based Fuzzy C-Means: a Method to Apply KFCM on Crisp and Non-Crisp Numbers

Kernel methods are a class of algorithms for pattern analysis to robust them to noise, overlaps, outliers and also unequal sized clusters. In this paper, kernel-based fuzzy c-means (KFCM) method is extended to apply KFCM on any crisp and non-crisp input numbers only in a single structure. The proposed vectorized KFCM (VKFM) algorithm maps the input (crisp or non-crisp) features to crisp ones and applies the KFCM (with prototypes in feature space) on them. Finally the resulted crisp prototypes in the mapped space are influenced by an inverse mapping to obtain the prototypes’ (centers’) parameters in the input features space. The performance of the proposed method has been compared with the conventional FCM and KFCM and other new methods, to show its effectiveness in clustering of gene expression data and segmentation of land-cover using satellite images. Simulation results show good accuracy of proposed method in compare to other methods.
k-Attempt Thinning

Thinning is a frequently used approach to produce all kinds of skeleton-like shape features in a topology-preserving way. It is an iterative object reduction: some border points of binary objects are deleted, and the entire process is repeated until stability is reached. In the conventional implementation of thinning algorithms, we have to investigate the deletability of all border points in each iteration step. In this paper, we introduce the concept of k-attempt thinning ( $$k\ge 1$$ ). In the case of a k-attempt algorithm, if a border point ‘survives’ at least k successive iterations, it is ‘immortal’ (i.e., it belongs to the produced feature). We propose a computationally efficient implementation scheme for k-attempt thinning. It is shown that an existing parallel thinning algorithm is 5-attempt, and the advantage of the new implementation scheme over the conventional one is also illustrated.
Open City Museum: Unveiling the Cultural Heritage of Athens Through an -Augmented Reality Based- Time Leap

A nation’s cultural heritage is of great importance both for indigenous people as well as for foreigners as it is a nation’s contribution to humanity and global civilization. As the spark to discover that treasure has ignited and travelling around the world has become much easier, it is necessary to enhance the way that monuments are exhibited and communicated. Current technology offers the capability to alter the way that information is provided and represented. Augmented reality (AR) is the most characteristic example as it surpasses the limits that exist in other media offering a unique experience to the user. A great challenge for AR is to shed a new light on monuments, especially in cities like Athens, filled with historical monuments. In this paper we describe the methodology that was followed in order to create an AR application that will provide users with a virtual time leap experience in the past depicting the monument’s history in its social context. Thus, the issues emerging during the development of an AR app is discussed, as well as solutions to common problems regarding its utilization in outdoors space. Aiming to shed light on its importance throughout the centuries, an interdisciplinary research has been conducted combining fields like architecture and psychology in order to inform and in parallel, arouse emotions to visitors and thus, intensify the experience. Supporting that, user experience has been enhanced following a UI/UX approach, which provides the appropriate tools between easy-to-use and following a narrative.
Virtual Kayaking: A Local Culture-Based Virtual Reality Paddling Experience

An activity in a well-designed virtual environment can provide both a short-term emotional impression and an immersive experience related to personal and cultural experience. In this study, we dissect the “Coming-of-Sixteen” (COS) ritual, a local rite of passage that derives from the Tainan city of Taiwan, and meticulously select kayaking activity as the theme among other related activities. Next, we propose a virtual kayaking experience (VKE) system that includes the physical movement of users using virtual reality (VR) techniques. We adopt real scenes of tradition-based culture as the vision of VR to evoke the motivation of the elderly, and also engage bystanders in the experience through these familiar scenes. To evaluate the feasibility of applying the VKE system to the rehabilitation of elderly people, we conduct two formative user-studies. The results suggest that virtual reality technology provides an opportunity to leverage culturally relevant activities, which can enhance the engagement of the elderly rehabilitation.
Enabling ad-hoc reuse of private data repositories through schema extraction

Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) impose strict requirements concerning the protection of personal and privacy sensitive data. Therefore new approaches, such as the Personal Health Train initiative, are emerging to utilize data right in their original repositories, circumventing the need to transfer data. Circumventing limitations of previous systems, this paper proposes a configurable and automated schema extraction and publishing approach, which enables ad-hoc SPARQL query formulation against RDF triple stores without requiring direct access to the private data. The approach is compatible with existing Semantic Web-based technologies and allows for the subsequent execution of such queries in a safe setting under the data provider’s control. Evaluation with four distinct datasets shows that a configurable amount of concise and task-relevant schema, closely describing the structure of the underlying data, was derived, enabling the schema introspection-assisted authoring of SPARQL queries. Automatically extracting and publishing data schema can enable the introspection-assisted creation of data selection and integration queries. In conjunction with the presented system architecture, this approach can enable reuse of data from private repositories and in settings where agreeing upon a shared schema and encoding a priori is infeasible. As such, it could provide an important step towards reuse of data from previously inaccessible sources and thus towards the proliferation of data-driven methods in the biomedical domain.
Word Transcription of MODI Script to Devanagari Using Deep Neural Network

Many historical documents and letters are written in MODI script. Study of “Shivakalin” and “Peshvekalin” era documents is almost impossible without the knowledge of MODI script. This work aims to bridge the gap between Devanagari and MODI Script by developing a system to map the recognized MODI characters to its Devanagari equivalent. Our dataset would comprise 57 different classes of MODI Script characters. The various approaches for feature extraction usually used include moment invariant, affine moment invariant, chain code histogram, intersection junction and for character classification include SVM and KNN classifiers. Deep Neural Networks on the other hand do not require any feature to be explicitly defined, instead they work on the raw pixel data to generate the best features and use them to classify the inputs into different classes. Hence, we propose a deep learning architecture for character recognition. CNN uses little pre-processing compared to other image classification algorithms. This means that the network learns the filters which in traditional algorithms were hand-engineered. The system aims to provide a good recognition rate by implementing CNN.