Reviewerly/exHarmony huggingface dataset:
Precision in spatial working memory examined with mouse pointing

Authors:
raw_author_name: Daniel T. Smith
raw_affiliation_string: Department of Psychology, Durham University, United Kingdom

Abstract: The capacity of visuospatial working memory (VSWM) is limited. However, there is continued debate surrounding the nature of this capacity limitation. The resource model (Bays et al., 2009) proposes that VSWM capacity is limited by the precision with which visuospatial features can be retained. In one of the few studies of spatial working memory, Schneegans and Bays (2016) report that memory guided pointing responses show a monotonic decrease in precision as set size increases, consistent with resource models. Here we report two conceptual replications of this study that use mouse responses rather than pointing responses. Overall results are consistent with the resource model, as there was an exponential increase in localisation error and monotonic increases in the probability of misbinding and guessing with increases in set size. However, an unexpected result of Experiment One was that, unlike Schneegans and Bays (2016), imprecision did not increase between set sizes of 2 and 8. Experiment Two replicated this effect and ruled out the possibility that the invariance of imprecision at set sizes greater than 2 was a product of oculomotor strategies during recall. We speculate that differences in imprecision are related to additional visuomotor transformations required for memory-guided mouse localisation compared to memory-guided manual pointing localisation. These data demonstrate the importance of considering the nature of the response modality when interpreting VSWM data.
Customer sentiment recognition in conversation based on bidirectional LSTM and self-attention mechanism

Authors:
raw_author_name: Zhengwei Huang
raw_affiliation_string: College of Economics and Management, China Three Gorges University, Yichang, Hubei, China
raw_author_name: Huayuan Liu
raw_affiliation_string: College of Economics and Management, China Three Gorges University, Yichang, Hubei, China
raw_author_name: Chen Duan
raw_affiliation_string: College of Economics and Management, China Three Gorges University, Yichang, Hubei, China
raw_author_name: Jintao Min
raw_affiliation_string: College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, China

Abstract: In the E-commerce environment, conversations between customers and businesses contain lots of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. For conversational sentiment analysis, most existing approaches take contextual information into account. On this basis, we focus on the degree of association between utterances, which can more effectively capture overall and useful sentiment information in conversation. For this purpose, we propose a hybrid model to recognize customer sentiment in conversation. The model obtains utterance vectors with sentiment information through Sentiment Knowledge Enhanced Pre-training (SKEP), then uses the bidirectional long short-term memory network (BiLSTM) to generate contextual semantic information, and further obtains customer sentiment information by applying the self-attention mechanism to focus on the degree of association between utterances. The experimental results on the JD Dialog dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.
Scalable and resilient 360-degree-video adaptive streaming over HTTP/2 against sudden network drops

Authors:
raw_author_name: Viet Hung Nguyen
raw_affiliation_string: Faculty of Information Technology, East Asia University of Technology, Viet Nam; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam
raw_author_name: Duy Tien Bui
raw_affiliation_string: School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam
raw_author_name: Cong Thang Truong
raw_affiliation_string: The University of Aizu, Aizuwakamatsu, Japan
raw_author_name: Thu Huong Truong
raw_affiliation_string: School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam

Abstract: Realistic Virtual Reality is supported by 360° video, which provides viewers with an immersive watching experience. However, 360°video is bulky in size, while the transmission system has limited ability to provide bandwidth. As such, intelligent adaptive delivery solutions play a crucial role in enabling users to stream high-quality 360°video. In this research, we propose a novel approach for 360-degree video streaming over HTTP/2 that can provide consumers with a good watching experience (QoE) even in varying network circumstances and head-eye movements over time. The proposed method deploys the so-called BBAG algorithm (Buffer and Bandwidth Allocation Algorithm) using Scalable Video Coding to choose appropriate tile layers to resolve the trade-off between network and user adaptivity. With the support of HTTP/2's stream termination capability, the delivery of late tile layers is terminated to handle abrupt interruptions. By employing multiple buffer thresholds, BBAG is able to adapt bitrates to changes in users' perspective while watching a 360-degree video. BBAG is proven to improve QoE up to about 90% by maintaining high and stable buffer level, while enhancing average viewport bitrates by about 80% compared to state-of-the-art methods in different scenarios of network bandwidths.
Sensor-based agitation prediction in institutionalized people with dementia A systematic review

Authors:
raw_author_name: Jan Kleine Deters
raw_affiliation_string: Sensors and Smart Systems Group, Institute of Engineering, Hanze University of Applied Sciences, Groningen, The Netherlands
raw_author_name: Sarah Janus
raw_affiliation_string: Alzheimer Center Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Primary and Long-term care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
raw_author_name: Heinrich J. Wörtche
raw_affiliation_string: Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; Sensors and Smart Systems Group, Institute of Engineering, Hanze University of Applied Sciences, Groningen, The Netherlands
raw_author_name: Sytse U. Zuidema
raw_affiliation_string: Alzheimer Center Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Primary and Long-term care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

Abstract: Early detection of agitation in individuals with dementia can lead to timely interventions, preventing the worsening of situations and enhancing their quality of life. The emergence of multi-modal sensing and advances in artificial intelligence make it feasible to explore and apply technology for this goal. We conducted a literature review to understand the current technical developments and challenges of its integration in caregiving institutions. Our systematic review used the Pubmed and IEEE scientific databases, considering studies from 2017 onwards. We included studies focusing on linking sensor data to vocal and/or physical manifestations of agitation. Out of 1622 identified studies, 12 were selected for the final review. Analysis was conducted on study design, technology, decisional data, and data analytics. We identified a gap in the standardized semantic representation of both behavioural descriptions and system event generation configurations. This research highlighted initiatives that leverage existing information in a caregiver's routine, such as correlating electronic health records with sensor data. As predictive systems become more integrated into caregiving routines, false positive reduction needs to be addressed as those will discourage their adoption. Therefore, to ensure adaptive predictive capacity and personalized system re-configuration, we suggest future work to evaluate a framework that incorporates a human-in-the-loop approach for detecting and predicting agitation.
An Efficient Hybrid Deep Learning Accelerator for Compact and Heterogeneous CNNs

Authors:
raw_author_name: Fareed Qararyah
raw_affiliation_string: Chalmers University of Technology, Sweden
raw_author_name: Muhammad Waqar Azhar
raw_affiliation_string: Chalmers University of Technology, Sweden
raw_author_name: Pedro Trancoso
raw_affiliation_string: Chalmers University of Technology, Sweden

Abstract: Resource-efficient Convolutional Neural Networks (CNNs) are gaining more attention. These CNNs have relatively low computational and memory requirements. A common denominator among such CNNs is having more heterogeneity than traditional CNNs. This heterogeneity is present at two levels: intra-layer type and inter-layer type. Generic accelerators do not capture these levels of heterogeneity, which harms their efficiency. Consequently, researchers have proposed model-specific accelerators with dedicated engines. When designing an accelerator with dedicated engines, one option is to dedicate one engine per CNN layer. We refer to accelerators designed with this approach as single-engine single-layer (SESL). This approach enables optimizing each engine for its specific layer. However, such accelerators are resource-demanding and unscalable. Another option is to design a minimal number of dedicated engines such that each engine handles all layers of one type. We refer to these accelerators as single-engine multiple-layer (SEML). SEML accelerators capture the inter-layer-type but not the intra-layer-type heterogeneity. We propose the Fixed Budget Hybrid CNN Accelerator (FiBHA), a hybrid accelerator composed of an SESL part and an SEML part, each processing a subset of CNN layers. FiBHA captures more heterogeneity than SEML while being more resource-aware and scalable than SESL. Moreover, we propose a novel module, Fused Inverted Residual Bottleneck (FIRB), a fine-grained and memory-light SESL architecture building block. The proposed architecture is implemented and evaluated using high-level synthesis (HLS) on different Field Programmable Gate Arrays representing various resource budgets. Our evaluation shows that FiBHA improves the throughput by up to 4 x and 2.5 x compared to state-of-the-art SESL and SEML accelerators, respectively. Moreover, FiBHA reduces memory and energy consumption compared to an SEML accelerator. The evaluation also shows that FIRB reduces the required memory by up to 54%, and energy requirements by up to 35% compared to traditional pipelining.
Optimization of Expert System Based on Interpolation, Forward Chaining, and Certainty Factor for Diagnosing Abdominal Colic

Authors:
raw_author_name: Hari Soetanto
raw_author_name: None Painem

Abstract: Abdominal colic is a common condition that affects infants and it can be difficult to diagnose because it shares many symptoms with other conditions, such as gastric disease and appendicitis. Limitations of existing diagnostic methods include the unreliability of physical examinations and medical histories and the high cost and time-consuming nature of imaging tests. This research proposes an expert system based on interpolation, forward chaining, and certainty factors for diagnosing abdominal colic. This system has the potential to provide a more accurate and efficient way to diagnose abdominal colic, which could lead to better patient outcomes. This research proposes an expert system based on interpolation, forward chaining, and certainty factors for diagnosing abdominal colic. This system is implemented as a web application model. The forward chaining method is used to establish rules for the expert system. The rules are based on the symptoms and diseases that are included in the system&#039;s knowledge base. The interpolation method is used to normalize lab results and the certainty factor method is used to process medical history and physical examinations. This is necessary because medical history and physical examinations can be imprecise. The expert system was tested on a dataset of 100 cases and it was able to accurately diagnose 96 patients, achieving a 96% accuracy rate. This suggests that the expert system has the potential to provide a more accurate and efficient way to diagnose abdominal colic, which could lead to better patient outcomes.
Playing With Monte-Carlo Tree Search [AI-eXplained]

Authors:
raw_author_name: Yunlong Zhao
raw_affiliation_string: Southern University of Science and Technology, Shenzhen, China
raw_author_name: Chengpeng Hu
raw_affiliation_string: Southern University of Science and Technology, Shenzhen, China
raw_author_name: Jialin Liu
raw_affiliation_string: Southern University of Science and Technology, Shenzhen, China

Abstract: This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore.
Omniscient Video Super-Resolution with Explicit-Implicit Alignment

Authors:
raw_author_name: Peng Yi
raw_affiliation_string: School of Computer Science, Wuhan University, China
raw_author_name: Zhongyuan Wang
raw_affiliation_string: School of Computer Science, Wuhan University, China
raw_author_name: Laigan Luo
raw_affiliation_string: The Electronic Information School, Wuhan University, China
raw_author_name: Kui Jiang
raw_affiliation_string: School of Computer Science, Wuhan University, China
raw_author_name: Zheng He
raw_affiliation_string: School of Computer Science, Wuhan University, China
raw_author_name: Junjun Jiang
raw_affiliation_string: The School of Computer Science and Technology, Harbin Institute of Technology, China
raw_author_name: Tao Lu
raw_affiliation_string: The School of Computer Science and Engineering, Wuhan Institute of Technology, China
raw_author_name: Jiayi Ma
raw_affiliation_string: The Electronic Information School, Wuhan University, China

Abstract: When considering the temporal relationships, most previous video super-resolution (VSR) methods follow the iterative or recurrent framework. The iterative framework adopts neighboring low-resolution (LR) frames from a sliding window, while the recurrent framework utilizes the output generated in the previous SR procedure. The hybrid framework combines them but still cannot fully leverage the temporal relationships. Meanwhile, the existing methods are limited in the receptive field of the optical flow or lack semantic constrains on motion information. In this work, we propose an omniscient framework to fully explore the temporal relationships in the video, which encompasses both LR frames and SR outputs from the past, present, and future. The omniscient framework is more generic because the iterative, recurrent, and hybrid frameworks can be regarded as its special cases. Besides, when addressing the motion information, most previous VSR methods adopt the explicit motion estimation and compensation, while many recent methods turn to implicit alignment. In implicit alignment methods, because basic non-local means suffers from heavy computational costs, we improve it by capturing the non-local correlations in a relatively local manner to reduce the complexity. Moreover, we integrate the explicit and implicit methods into an explicit-implicit alignment module to better utilize motion information. We have conducted extensive experiments on public datasets, which show that our method is superior over the state-of-the-art methods in objective metrics, subjective visual quality, and complexity. In particular, on datasets of Vid4 and UDM10, our method improves PSNR by 0.19 dB, 0.49 dB against the most advanced method BasicVSR++, respectively.
Knowledge graph embedding in a uniform space

Authors:
raw_author_name: Da Tong
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China; School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Shudong Chen
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China; School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Rong Ma
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
raw_author_name: Donglin Qi
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China; School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Yong Yu
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China; School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, China

Abstract: Knowledge graph embedding (KGE) is typically used for link prediction to automatically predict missing links in knowledge graphs. Current KGE models are mainly based on complicated mathematical associations, which are highly expressive but ignore the uniformity behind the classical bilinear translational model TransE, a model that embeds all entities of knowledge graphs in a uniform space, enabling accurate embeddings. This study analyses the uniformity of TransE and proposes a novel KGE model called ConvUs that follows uniformity with expressiveness. Based on the convolution neural network (CNN), ConvUs proposes constraints on convolution filter values and employs a multi-layer, multi-scale CNN architecture with a non-parametric L2 norm-based scoring function for the calculation of triple scores. This addresses potential uniformity-related issues in existing CNN-based KGE models, allowing ConvUs to maintain a uniform embedding space while benefiting from the powerful expressiveness of CNNs. Furthermore, circular convolution is applied to alleviate the potential orderliness contradictions, making ConvUs more suitable for conducting uniform space KGE. Our model outperformed the base model ConvKB and several baselines on the link prediction benchmark WN18RR and FB15k-237, demonstrating strong applicability and generalization and indicating that the uniformity of embedding space with high expressiveness enables more efficient knowledge graph embeddings.
Corrigendum to “Omnisolver: An extensible interface to Ising spin–glass and QUBO solvers” [SoftwareX 24 (2023) 101559]

Authors:
raw_author_name: K. Jałowiecki
raw_affiliation_string: Institute of Theoretical and Applied Informatics, Polish Academy of Sciences
raw_author_name: Ł. Pawela
raw_affiliation_string: Institute of Theoretical and Applied Informatics, Polish Academy of Sciences

Abstract: The authors regret imprecise statement of acknowledgements. The acknowledgement section should contain the following statement: “This work is supported by the project “Near-term quantum computers Challenges, optimal implementations and applications” under Grant Number POIR.04.04.00–00–17C1/18–00, which is carried out within the Team-Net programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.” The authors would like to apologise for any inconvenience caused. Omnisolver: An extensible interface to Ising spin–glass and QUBO solversSoftwareXVol. 24PreviewWe introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing the effort required for implementing new algorithms for solving BQMs. The proposed software should be of benefit for researchers focusing on quantum annealers or discrete optimization algorithms as well as groups utilizing discrete optimization as a part of their daily work. Full-Text PDF Open Access
Optimized in-loop filtering in versatile video coding using improved fast guided filter

Authors:
raw_author_name: Purnachand Nalluri

Abstract: &lt;p&gt;Devices with varying display capabilities from a common source may face degradation in video quality because of the limitation in transmission bandwidth and storage. The solution to overcome this challenge is to enrich the video quality. For the mentioned purpose, this paper introduces an improved fast guided filter (IFGF) for the contemporary video coding standard H.266/VVC (versatile video coding), a continuation of H.265/HEVC (high efficiency video coding). VVC includes several types of coding techniques to enhance video coding efficiency over existing video coding standards. Despite that, blocking artifacts are still present in the images. Hence, the proposed method focuses on denoising the image and the increase of video quality, which is measured in terms of peak signal-to-noise (PSNR). The objective is achieved by using an IFGF for in-loop filtering in VVC to denoise the reconstructed images. VTM (VVC test model)-17.2 is used to simulate the various video sequences with the proposed filter. This method achieves a 0.67% Bjontegaard delta (BD)-rate reduction in low-delay configuration accompanied by an encoder run time increase of 4%.&lt;/p&gt;
Security enhancement of cyber-physical system using modified encryption AESGNRSA technique

Authors:
raw_author_name: Kundankumar Rameshwar Saraf
raw_author_name: P. Malathi

Abstract: &lt;span&gt;A cyber-physical system (CPS) is a combination of physical components with computational elements to interact with the physical world. The integration of these two systems has led to an increase in security concerns. Traditional encryption algorithms designed for general-purpose computing environments may not adequately address the distinct challenges of CPS, such as limited processing power, delay, and resource-constrained hardware. Therefore, there is a pressing need to develop an encryption algorithm that is optimized for CPS security without compromising the critical real-time aspects of these systems. This research has designed a modified encryption technique named the advanced encryption standard in galois counter mode with nonce and rivest-shamir-adleman algorithm (AESGNRSA). A smart medical system is designed to monitor the health of remotely located patients. The AESGNRSA algorithm is applied to the three servers of this system. The data of 1.5 lakh patients is fed to this system to verify the effectiveness of the AESGNRSA algorithm. The performance parameters like encryption and decryption time, encryption and decryption throughput, and encrypted file size are calculated for the AESGNRSA algorithm. The comparative analysis proved that AESGNRSA has the highest performance as compared to other algorithms and it can protect CPS against many cyber-attacks.&lt;/span&gt;