Reviewerly/exHarmony huggingface dataset:
Improving Logistics of the Public Services in Smart Cities Using a Novel Clustering Method

Smart City public services need detailed and relevant public information to increase their efficiency. To have relevant information, collecting and processing the data about its previous uses are crucial. Clustering is one of the most powerful, yet computationally demanding, tools that can be used to process such information. Since public services data are vast, but usually not accurate, the objects clustered are considered as uncertain. In this paper, we propose a novel clustering method for uncertain objects called Improved Bisector Pruning (IBP), which uses bisectors to reduce the number of computations. We combine IBP with a modified segmentation of a data set area (SDSA) method that enables the parallelization of the clustering process. In the experiments, we show that IBP-SDSA is superior in performance to the most used clustering method UK-means combined with Voronoi or MinMax pruning, regardless of the problem size. We applied IBP-SDSA on clustering the public services data in the city of Osijek and show that the acquired data can be used to improve public services logistics.
Dynamic Quantization of Digital Filter Coefficients

The possibility of quantizing the coefficients of a digital filter in the concept of dynamic mathematical&#x0D; programming, as a dynamic process of step-by-step quantization of coefficients with their discrete optimization at&#x0D; each step according to the objective function, common to the entire quantization process, is considered. Dynamic&#x0D; quantization can significantly reduce the functional error when implementing the required characteristics of a lowbit digital filter in comparison with classical quantization. An algorithm is presented for step-by-step dynamic&#x0D; quantization using integer nonlinear programming methods, taking into account the specified signal scaling and the&#x0D; radius of the poles of the filter transfer function. The effectiveness of this approach is illustrated by dynamically&#x0D; quantizing the coefficients of a cascaded high-order IIR bandpass filter with a minimum bit depth to represent integer&#x0D; coefficients. A comparative analysis of functional quantization errors is carried out, as well as a test of the quantized&#x0D; filter performance on test and real signals.
Sentiment analysis based on food e-commerce reviews

Abstract Customer feedback is of great significance for food manufacturers such as wine to improve product quality. With the increase of consumer users, user feedback collection and classification methods in the traditional food industry have become very time-consuming. Aiming at this problem, this paper proposes a method of embedding TD-IDF weighted Word2vec word vector into a bidirectional long and short-term memory network based on the attention mechanism.
Age group and gender classification using convolutional neural networks with a fuzzy logic-based filter method for noise reduction

Biometry is the science that enables living things to be distinguished by examining their physical and behavioral characteristics. The facial recognition system (FCS) is a kind of biometric system. FCS provides a unique mathematical model by determining the distance between the cheekbones, chin, nose, eyes, jawline, and similar positions using the facial features of the persons. Determining the gender and age group of chosen persons’ from face images is the main purpose of this study. It is targeted to distinguish the gender of the person and to obtain information about the person is children or adults by making essential works on the images. Convolutional neural network (CNN) is one of the deep face recognition algorithms that widely used to recognize facial images. This study is suggested as a study that detects noise in images using the fuzzy logic-based filter method and classifies this cleared data by gender using the matrix completion and CNN. TensorFlow which is a machine learning library that used to train and tests deep learning methods is used for experiments. The customer photographs taken during using the system are transformed into a matrix expression through a system trained using this algorithm. The obtained results indicated that the offered technique detects age and gender with a 96% accuracy value and 1.145 seconds time.
Distributed Graph Processing System and Processing-in-memory Architecture with Precise Loop-carried Dependency Guarantee

To hide the complexity of the underlying system, graph processing frameworks ask programmers to specify graph computations in user-defined functions (UDFs) of graph-oriented programming model. Due to the nature of distributed execution, current frameworks cannot precisely enforce the semantics of UDFs, leading to unnecessary computation and communication. It exemplifies a gap between programming model and runtime execution. This article proposes novel graph processing frameworks for distributed system and Processing-in-memory (PIM) architecture that precisely enforces loop-carried dependency; i.e., when a condition is satisfied by a neighbor, all following neighbors can be skipped. Our approach instruments the UDFs to express the loop-carried dependency, then the distributed execution framework enforces the precise semantics by performing dependency propagation dynamically. Enforcing loop-carried dependency requires the sequential processing of the neighbors of each vertex distributed in different nodes. We propose to circulant scheduling in the framework to allow different nodes to process disjoint sets of edges/vertices in parallel while satisfying the sequential requirement. The technique achieves an excellent trade-off between precise semantics and parallelism—the benefits of eliminating unnecessary computation and communication offset the reduced parallelism. We implement a new distributed graph processing framework SympleGraph, and two variants of runtime systems— GraphS and GraphSR —for PIM-based graph processing architecture, which significantly outperform the state-of-the-art.
Linear regression strategy for differential evolution

A new Differential Evolution algorithm is proposed which incorporates a linear regression strategy to find the best single-objective optimization solution. Furthermore, this algorithm contemplates directional information and classical chromosome conservation strategies. Its main motivation is to be used in problems in which it is computationally hard to calculate the fitness differences among solutions.
Designing for/with/around Nature

Resonating with the CHItaly theme "Frontiers of HCI", this workshop will explore the frontiers of i) using technology outdoors, in terms of scope, opportunities, and limitations; ii) spaces that are not completely natural nor artificial but hybrid and offer opportunities for remote and asynchronous outdoor experiences; iii) interaction design in accounting for human interests over a variety of non-human ecologies.
Privacy preserving getup detection

The ageing population leads to an increase of people requiring long-term care. Assisting people when getting out of bed and fast reactions to falls can help to reduce costs and the risk of injury. We describe the possibility of detecting getting up behavior from a bed using different deep learning models and depth data as a proof of concept. The hereby used computer vision approach uses unobtrusive depth data in order to protect people’s privacy. We gather data from different subjects, postures, views and rooms, with which we then train the network. Both classification and object detection methods are able to reliably detect getting up behavior. Situations that could not be correctly classified were when a person was changing from lying to sitting or when the legs of the person were covered with a blanket. Our results show that using pretrained networks is the key contributor in training. We also demonstrate that convolutional neural networks are capable of extracting high-level task-dependent features from depth data which can be utilized in developing ambient intelligent systems. The practicality of the network can be adapted from getting up from a bed to sitting and walking inside the room, based on the purpose of the real-life application.
Application of multivariate canonical harmonic trend analysis, singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing mapping to identify geochemical anomaly related to iron polymetallic mineralization in Hunjiang district, Northeastern China

How to more effectively perform anomaly detection of combination information has always been an important issue for the scholars in various fields. In order to identify and extract the geochemical anomaly information related to polymetallic mineralization in the Hunjiang area, this article uses the hybrid method that combines multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing map (IAFSOM). First, multiple sets of combination feature information with multi-dimensional variables will be obtained through the MCHTA method, which information is considered as the initial information for the subsequent analysis. Next, the singularity analysis method is used to process the combination concentration value to calculate the singularity indexes. Finally, the singularity indexes are classified by the IAFSOM method, and nine groups of sample data are obtained. The analysis results found that the samples information in fourth group covered most of the low α-values. The main conclusions in this study are as follows: (1) The MCHTA method can effectively detect the combination information related to geochemical anomaly; (2) The application of singularity analysis method with radius-areal metal amount can reveal the significant characteristics of mineralization combination elements; (3) IAFSOM can be used as an effective tool for the classification and identification of geochemical anomaly with combination information; (4) the hybrid method that combines MCHTA method, singularity analysis and IAFSOM model has a good indication significance in the prospecting of geochemical anomalies, and could provide a good method for geochemical prospecting.
Study On Machine Learning Algorithms

Various machine learning algorithms are described in this work. These algorithms are used for a variety of applications, including data mining, image processing, predictive analytics, and so on. The fundamental benefit of employing machine learning is that once an algorithm learns what to do with data, it can complete tasks on its own.
Evaluation of RGB Quantization Schemes on Histogram-Based Content Based Image Retrieval

Colour feature indexing for images has seen several approaches such as Conventional Colour Histogram, Colour Coherent Vector, Colour Moment and Colour Correlogram. These approaches for indexing images have proven to be fast, simple, and retrieve images from database with satisfactory results. The strength of these approaches however is based on the colour space and quantization schemes employed for the indexing of images. Various works have explored colour spaces and quantization for CBIR applications and have reported that the RGB colour space for CBIR sometime suffers some inefficiencies in retrieval accuracies. Interestingly, almost all the experiments used images that were converted from RGB colour space. Mathematical formulas were used to perform this conversion of RGB colour space to the other spaces. This suggests that RGB colour space may not necessarily be a poorer colour space for CBIR application, but the choice of quantization affects its performance with CBIR task. This work therefore evaluated various quantization schemes (uniform and non-uniform) to determine which of the schemes perform best for histogram based CBIR application. Results show that CBIR developers can opt for RGB quantization schemes in the combination of 4s and 8s bins on each of the colour channel or band for optimum retrieval.
Self‐adaptation and distributed knowledge‐based service ecosystem evolution

Summary Web services (or Web APIs) on the Internet tends to encounter various unexpected runtime failures because of their dynamicity and distribution. Self‐adaptation technologies for the service‐based business process can effectively repair runtime failures and improve its success rate. However, the same failures may occur on subsequent invocations because relevant processes do not evolve after failures. This makes the response time of the business processes too long. We proposed a self‐adaptation and distributed knowledge‐based evolution model (SDKEM) to guarantee business processes' stabilities, that is, low failure rates and stable response time. SDKEM adopts a service knowledge base (SKB) to organize services from a provider and uses bridge rules to eliminate semantic conflicts among multiple distributed SKBs. It can automatically trigger the evolution of a service ecosystem through the designed self‐adaptation mechanism. We adopt the “survival of the fittest” principle for crucial elements in the ecosystem during evolution so that ultimately, service‐based processes and services with high stability remain. Experiments show that, with the developed evolution mechanism, runtime failures of business processes significantly reduce. In most cases, their response time and success rates are comparable to those under the running situation where no runtime failure occurs, meaning the runtime failures within a service‐based process are automatically repaired.
Semantic meaning modulates object importance in human fixation prediction

Humans tend to allocate attention to semantic entities. Objects are important in fixation selection, but not all the objects are equally attractive. In this paper, we introduce the concept of attribute bias to characterize the influence of semantic attributes compared with low-level saliency on fixation distribution. Two different ways are adopted to get two sets of semantic attributes. In both cases, most semantic attributes have a positive influence on drawing attention and contribute more than low-level saliency in object areas. We also find that attribute bias is robust to low-level saliency and can consistently reflect the relative attractiveness of objects with different semantic attributes. It is demonstrated that such bias helps make better fixation predictions by distinguishing the importance of objects, although low-level saliency models with better performance are less dramatically improved by attribute bias. These findings indicate the role of conceptual meaning as opposed to features in visual attention.
Towards Semantic Comparison of Concept Maps for Structuring Learning Activities

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students’ assessment and many others. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appear appropriate for concept maps. In concept maps, concepts exposed are at least as relevant as the structure that contains them. Neglecting the semantic and didactic aspect inevitably causes inaccuracies and the consequently limited applicability in automated systems. In this work, starting from an algorithm which compares didactic characteristic of concept maps, we present an extension which exploits a semantic approach to catch the actual meaning of the concepts expressed in the nodes of the map. We also present experimental results.
Computer Scientist’s and Programmer’s View on Quantum Algorithms: Mapping Functions’ APIs and Inputs to Oracles

Quantum Computing (QC) is a promising approach which is expected to boost the development of new services and applications. Specific addressable problems can be tackled through acceleration in computational time and advances with respect to the complexity of the problems, for which QC algorithms can support the solution search. However, QC currently remains a domain that is strongly dominated by a physics’ perspective. Indeed, in order to bring QC to industrial grade applications we need to consider multiple perspectives, especially the one of software engineering and software application/service programming. Following this line of thought, the current paper presents our computer scientist’s view on the aspect of black-box oracles, which are a key construct for the majority of currently available QC algorithms. Thereby, we observe the need for the input of API functions from the traditional world of software engineering and (web-)services to be mapped to the above mentioned black-box oracles. Hence, there is a clear requirement for automatically generating oracles for specific types of problems/algorithms based on the concrete input to the belonging APIs. In this paper, we discuss the above aspects and illustrate them on two QC algorithms, namely Deutsch-Jozsa and the Grover’s algorithm.
Three population co-evolution for generating mechanics of endless runner games

Procedural content generation (PCG) is increasingly used to generate many aspects in a variety of games. However, using PCG to generate mechanics of games is rarely attempted. Past approaches include using cooperative coevolution to generate the rules and the environment of a game, which had interesting results. Our approach extends this idea by using coevolution with three populations, also generating the evaluating player. We used this approach to generate endless runner games, for which it was able to generate novel mechanics.
LDAvis, BERT: Comparison of Method Application Under Corona Conditions

In a previous modeling study, the automatic topic modeling method LDAvis was used to identify the thematic structure of a local discourse. Through the analysis, it became possible to determine which issues are relevant to the community in the period 2001–2020. The modeling specifications were adapted, whereby the nominalization of the medial linguistic data played a decisive role. The digital form of analysis of local discourse applied to southern Germany was applied to another local corpus and a nationwide corpus. The existing method is the basis for the configuration and validation (correlations and causalities) of a neural network with the aim of automating the process of identifying and evaluating local discourses. The results of the LDAvis modeling were validated using BERT calculations and found to be reliable. In this paper, the identified procedures are applied to new digitally accessible datasets and are tested again.
A Hierarchical Ontology for Dialogue Acts in Psychiatric Interviews

We present our work on modeling the context of an interview during diagnostic sessions for patients with mental health problems. The results are to be exploited by translation system for telehealth services. More specifically, we plan to use the context of the psychiatric interview in order to set informative priors over the vocabulary of the speaker. Therefore we have modelled the context with a hierarchical ontology, and we use it to classify the current state of the interview. The state is extracted after the doctor asks a question, and allow us to select a non-uniform prior regarding the vocabulary of the patient.
An Improved Object Detection Algorithm Based on CenterNet

CenterNet (Object as Points) is a recently popular single-stage anchor free object detection algorithm. First, this article adds a new attention module in which the mean and maximum values of the channel are used, it also introduces variance information to better express the feature distribution of each layer from different aspects. Second, we improved the loss function and replaced the original L1 loss function with the IoU loss function, so that the loss function of the borders and the center point regression has the scale invariance, which is consistent with the final standard IoU judgment, and will not increase the model inference time. Finally, multi-scale images are used for training. By enriching the number of samples of different sizes, the problem of model overfitting due to less data is effectively reduced. This paper compares the improved CenterNet algorithm with the original algorithm on the Pascal VOC data set for training and testing. The final model increases the mAP by 3.5% under the unaugmented test and 3.03% mAP for using horizontal flipping augmented test. In terms of time overhead, there is only a small expenditure, it can still maintain the detection speed of 12 ms per frame.
The Experimental Analysis on Transfer Function of Binary Particle Swarm Optimization

Binary Particle Swarm Optimization (BPSO) has extended the capacity of the conventional particle swarm optimization (PSO) for optimizing the discrete combinational optimization problems. The transfer function of BPSO is key for its capacity to search solution. This paper discuss the weight setting of two variants called the S-Shaped and the V-Shaped transfer functions in BPSO. The experimental results indicate that the increasing weight setting is beneficial to the performance of BPSO with the S-shaped transfer function, while the decreasing weight setting is flavorful for that of BPSO with the V-shaped transfer function. The Multi dimension Knapsack Problem (MKP) are used in the experiment for testing the discussable conclusions. The experimental results illustrate that the transfer function can be adjusted to improve the performance of BPSO, and the recommended weight setting is obtained, accordingly.
OpenUVR: an Open-Source System Framework for Untethered Virtual Reality Applications

Advancements in heterogeneous computing technologies enable the significant potential of virtual reality (VR) applications. To offer the best user experience (UX), a system should adopt an untethered, wireless-network-based architecture to transfer VR content between the user and the content generator. However, modern wireless network technologies make implementing such an architecture challenging, as VR applications require superior video quality-with high resolution, high frame rates, and very low latency. This paper presents OpenUVR, an open-source framework that uses commodity hardware components to satisfy the demands of interactive, real-time VR applications. OpenUVR significantly improves UX through a redesign of the system stack and addresses the most time-sensitive issues associated with redundant memory copying in modern computing systems. OpenUVR presents a cross-layered VR datapath to avoid redundant data operations and computation among system components, OpenUVR customizes the network stack to eliminate unnecessary memory operations incurred by mismatching data formats in each layer, and OpenUVR uses feedback from mobile devices to remove memory buffers. Together, these modifications allow OpenUVR to reduce VR application delays to 14.32 ms, meeting the 20 ms minimum latency in avoiding motion sickness. As an open-source system that is fully compatible with commodity hardware, OpenUVR offers the research community an opportunity to develop, investigate, and optimize applications for untethered, high-performance VR architectures.