Reviewerly/exHarmony huggingface dataset:
A parallel particle swarm optimization algorithm based on GPU/CUDA

Parallel computing is the main way to improve the computational efficiency of metaheuristic algorithms for solving high-dimensional, nonlinear optimization problems. Previous studies have typically only implemented local parallelism for the particle swarm optimization (PSO) algorithm. In this study, we proposed a new parallel particle swarm optimization algorithm (GPU-PSO) based on the Graphics Processing Units (GPU) and Compute Unified Device Architecture (CUDA), which uses a combination of coarse-grained parallelism and fine-grained parallelism to achieve global parallelism. In addition, we designed a data structure based on CUDA features and utilized a merged memory access mode to further improve data-parallel processing and data access efficiency. Experimental results show that the algorithm effectively reduces the solution time of PSO for solving high-dimensional, large-scale optimization problems. The speedup ratio increases with the dimensionality of the objective function, where the speedup ratio is up to 2000 times for the high-dimensional Ackley function.
Adaptive distributed multiple-model filter with uncertainty of process model

Target tracking is a critical application in multiple-sensor state estimation. However, it is subject to uncertainty in the process model due to target maneuvering, which includes motion model uncertainty and process noise uncertainty. In this paper, we present an adaptive distributed multiple-model filter for maneuvering target tracking that solves the two types of uncertainties by variational Bayesian. Initially, a centralized algorithm is developed over multiple-sensor. Using Categorical distribution and inverse Wishart distribution as the conjugate prior of motion model and process noise covariance respectively, the target state, model identity and noise parameters are jointly estimated by variational Bayesian. The algorithm is then extended to a distributed version where prior information and likelihood function achieve consensus through the Alternating Direction Method of Multipliers (ADMM). We also analyze the consensus and convergence of the proposed algorithm. The simulation results demonstrate the effectiveness of the proposed algorithm for maneuvering target tracking under process model uncertainty.
Efficient forecasting of chaotic systems with block-diagonal and binary reservoir computing

The prediction of complex nonlinear dynamical systems with the help of machine learning has become increasingly popular in different areas of science. In particular, reservoir computers, also known as echo-state networks, turned out to be a very powerful approach, especially for the reproduction of nonlinear systems. The reservoir, the key component of this method, is usually constructed as a sparse, random network that serves as a memory for the system. In this work, we introduce block-diagonal reservoirs, which implies that a reservoir can be composed of multiple smaller reservoirs, each with its own dynamics. Furthermore, we take out the randomness of the reservoir by using matrices of ones for the individual blocks. This breaks with the widespread interpretation of the reservoir as a single network. In the example of the Lorenz and Halvorsen systems, we analyze the performance of block-diagonal reservoirs and their sensitivity to hyperparameters. We find that the performance is comparable to sparse random networks and discuss the implications with regard to scalability, explainability, and hardware realizations of reservoir computers.
Ordering Services Modelling in Blockchain Platform for Food Supply Chain Management

Blockchain technology (BCT) is widely used in various applications where different stakeholders of their own interest are coming together where they cooperate and coordinate in trustless decentralized environment. The BCT is more suitable for supply chain management (SCM) applications. The food items supply chain is sensitive issue, and it is difficult to handle the problems food traceability, safety enable, authentication of stakeholders, security and consensus, transparency in transactions, food items shortfall, matching demand and supply chain wastage of material, etc. This research encourages for different stakeholders those are coming together on same platform and cooperate, coordinate each other to achieve common goal. The hyperledger fabric transaction flow is formed to record the world state by passing communication channels on peer to peer through farmers to distributors, distributors to food processing industry, food processing industry to distributors, distributors to retailers, and lastly retailers to customers. This proposed research uses core components of blockchain technology by designing specific architecture, writing smart contracts on Ethereum platform which provides facilitation for certain kind of food supply chain ordering services as a public permissioned platform. It uses blockchain platform along with properties of distributed and peer-to-peer supply chain architecture, and based on this, the analysis of SCM is shown through experimental results.
TMETA: Trust Management for the Cold Start of IoT Services With Digital-Twin-Aided Blockchain

There is growing attention in the metaverse from a variety of fields, and many Internet of Things (IoT) companies are exploring the possibility of integrating their existing business into the metaverse space. However, it still remains challenging to put the metaverse into practice. Among those challenges, trust management is critical to guarantee secure interactions and effective sharing among metaverse assets. In this article, we construct a TMETA system as a pioneer work for further establishing the metaverse and utilizing digital assets, which helps IoT startup (IS) companies expand their business during the cold start phase with limited business knowledge and seed budget. In the TMETA system, IoT companies treat their interaction experience with resource providers (RPs) as digital assets. When an IS initiates IoT services with the assistance of expertise-diverse RPs, task assignments are determined by leveraging the knowledge and trustworthy advice of expert IoT companies. With the aid of digital twin (DT) and blockchain technology, IoT tasks will be assigned to their suitable RPs as the secured instruction from trust DTs of expert companies, which are evaluated and updated by trust evolution and advice aggregation. We demonstrate our proposed TMETA system’s practicability with synthetic and real-world data. The experimental results indicate that our proposed TMETA system can help ISs attain more favorable outcomes and reduce the task failure rate in various trust environments, thereby accelerating the cold start process.
Structured software development versus agile software development: a comparative analysis

Abstract The use of agile methodology has become widespread in organizations that previously relied on traditional or structured software development methods, such as the waterfall approach. Successful completion rates for agile initiatives are at 40%, whereas only 15% of waterfall projects were successfully completed. To take advantage of the benefits of each method, many organizations are using a hybrid approach that combines Agile with Waterfall and structured software development methodologies. Rather than asking which methodology is superior, it is more productive to determine which one is most effective for a particular project. Agile projects have a failure rate of 10%, while waterfall projects fail 30% of the time. This study aims to compare and contrast agile and waterfall methodologies in order to create a decision tree for selecting the most suitable method for a software development project. Several cases and scenarios are examined using the hybrid development approach. Despite many advantages of Agile methodologies, a combination approach may be necessary, as Waterfall may be required for certain projects or development phases. This study explores the feasibility of combining Waterfall and Agile methodologies in software development management.
TwinkleTwinkle

Recent years have witnessed the rapid boom of mobile devices interweaving with changes the epidemic has made to people's lives. Though a tremendous amount of novel human-device interaction techniques have been put forward to facilitate various audiences and scenarios, limitations and inconveniences still occur to people having difficulty speaking or using their fingers/hands/arms or wearing masks/glasses/gloves. To fill the gap of such interaction contexts beyond using hands, voice, face, or mouth, in this work, we take the first step to propose a novel Human-Computer Interaction (HCI) system, TwinkleTwinkle, which senses and recognizes eye blink patterns in a contact-free and training-free manner leveraging ultrasound signals on commercial devices. TwinkleTwinkle first applies a phase difference based approach to depicting candidate eye blink motion profiles without removing any noises, followed by modeling intrinsic characteristics of blink motions through adaptive constraints to separate tiny patterns from interferences in conditions where blink habits and involuntary movements vary between individuals. We propose a vote-based approach to get final patterns designed to map with number combinations either self-defined or based on carriers like ASCII code and Morse code to make interaction seamlessly embedded with normal and well-known language systems. We implement TwinkleTwinkle on smartphones with all methods realized in the time domain and conduct extensive evaluations in various settings. Results show that TwinkleTwinkle achieves about 91% accuracy in recognizing 23 blink patterns among different people.
Development and validation of deep learning and BERT models for classification of lung cancer radiology reports

Manual cohort building from radiology reports can be tedious. Natural Language Processing (NLP) can be used for automated cohort building. In this study, we have developed and validated an NLP approach based on deep learning (DL) to select lung cancer reports from a thoracic disease management group cohort. 4064 radiology reports (CT and PET/CT) of a thoracic disease management group reported between 2014 and 2016 were used. These reports were anonymised, cleaned, text normalized and split into a training, testing, and validation set. External validation was performed on radiology reports from the MIMIC-III clinical database. We used three DL models, namely, Bi-LSTM_simple, Bi-LSTM_dropout, and Pre-trained _BERT model to predict if a report concerned lung cancer. We studied the effect of minority oversampling on all models. Without oversampling, the F1 scores at 95% CI for Bi-LSTM_simple, Bi-LSTM_dropout and BERT were 0.89, 0.90, and 0.86; with oversampling, the F1 scores were 0.94, 0.94, and 0.9, on internal validation. On external validation the F1-scores of Bi-LSTM_simple, Bi-LSTM_dropout and BERT models were 0.63, 0.77 and 0.80 without oversampling and 0.72, 0.78 and 0.77 with oversampling. Pre-trained BERT model and Bi-LSTM_dropout models to predict a lung cancer report showed consistent performance on internal and external validation with the BERT model exhibiting superior performance. The overall F1 score decreased on external validation for both Bi-LSTM models with the Bi-LSTM_simple model showing a more significant drop. All models showed some improvement on minority oversampling.
Enhancing Online UAV Multi-Object Tracking with Temporal Context and Spatial Topological Relationships

Multi-object tracking in unmanned aerial vehicle (UAV) videos is a critical visual perception task with numerous applications. However, existing multi-object tracking methods, when directly applied to UAV scenarios, face significant challenges in maintaining robust tracking due to factors such as motion blur and small object sizes. Additionally, existing UAV methods tend to underutilize crucial information from the temporal and spatial dimensions. To address these issues, on the one hand, we propose a temporal feature aggregation module (TFAM), which effectively combines temporal contexts to obtain rich feature response maps in dynamic motion scenes to enhance the detection capability of the proposed tracker. On the other hand, we introduce a topology-integrated embedding module (TIEM) that captures the topological relationships between objects and their surrounding environment globally and sparsely, thereby integrating spatial layout information. The proposed TIEM significantly enhances the discriminative power of object embedding features, resulting in more precise data association. By integrating these two carefully designed modules into a one-stage online MOT system, we construct a robust UAV tracker. Compared to the baseline approach, the proposed model demonstrates significant improvements in MOTA on two UAV multi-object tracking benchmarks, namely VisDrone2019 and UAVDT. Specifically, the proposed model achieves a 2.2% improvement in MOTA on the VisDrone2019 benchmark and a 2.5% improvement on the UAVDT benchmark.
LKC-Net: large kernel convolution object detection network

Deep learning-based object detection methods have achieved great performance improvement. However, since small kernel convolution has been widely used, the semantic feature is difficult to obtain due to the small receptive fields, and the key information cannot be highlighted, resulting in a series of problems such as wrong detection, missing detection, and repeated detection. To overcome these problems, we propose a large kernel convolution object detection network based on feature capture enhancement and vast receptive field attention, called LKC-Net. Firstly, a feature capture enhancement block based on large kernel convolution is proposed to improve the semantic feature capturing ability, and depth convolution is used to reduce the number of parameters. Then, the vast receptive filed attention mechanism is constructed to enhance channel direction information extraction ability, and it is more compatible with the proposed backbone than other existing attention mechanisms. Finally, the loss function is improved by introducing the SIoU, which can overcome the angle mismatch problem between the ground truth and prediction box. Experiments are conducted on Pascal VOC and MS COCO datasets for demonstrating the performance of LKC-Net.
Combined Bidirectional Long Short-Term Memory with Mel-Frequency Cepstral Coefficients Using Autoencoder for Speaker Recognition

Recently, neural network technology has shown remarkable progress in speech recognition, including word classification, emotion recognition, and identity recognition. This paper introduces three novel speaker recognition methods to improve accuracy. The first method, called long short-term memory with mel-frequency cepstral coefficients for triplet loss (LSTM-MFCC-TL), utilizes MFCC as input features for the LSTM model and incorporates triplet loss and cluster training for effective training. The second method, bidirectional long short-term memory with mel-frequency cepstral coefficients for triplet loss (BLSTM-MFCC-TL), enhances speaker recognition accuracy by employing a bidirectional LSTM model. The third method, bidirectional long short-term memory with mel-frequency cepstral coefficients and autoencoder features for triplet loss (BLSTM-MFCCAE-TL), utilizes an autoencoder to extract additional AE features, which are then concatenated with MFCC and fed into the BLSTM model. The results showed that the performance of the BLSTM model was superior to the LSTM model, and the method of adding AE features achieved the best learning effect. Moreover, the proposed methods exhibit faster computation times compared to the reference GMM-HMM model. Therefore, utilizing pre-trained autoencoders for speaker encoding and obtaining AE features can significantly enhance the learning performance of speaker recognition. Additionally, it also offers faster computation time compared to traditional methods.
Taking the advantage of Participatory Geographic Information System (P-GIS) in validating Semeru disaster prone area map in Oro-Oro Ombo, Lumajang

Abstract Desa Oro-Oro Ombo is located on the south-eastern flank of Semeru Volcano. It sits in a disaster-prone area (KRB) level I, II, and III due to the high possibility of being impacted by volcanic ash fall and lahar flow. However, the 1996 KRB map is no longer relevant because the impact of the 2021 Semeru eruption expanded more than expected. As a result, it increased the hazard and vulnerability of the village in the future. Therefore, it is crucial to generate the post-2021 KRB map and study the community’s perspective and knowledge about it. This research adopted a qualitative approach for data-gathering, i.e., in-depth interviews with key informants and the P-GIS methods. Interviews were conducted with the representative of BPBD Lumajang, coordinator of Semeru observatory station, coordinator of lahar flow observatory station, representative of local government, and impacted inhabitants. Meanwhile, P-GIS was applied to validate the KRB map. The study found a difference in the KRB coverage due to the deviation of the village administration boundaries. This research then produced a validated post-2021 Semeru eruption KRB map that can be utilized as the basis for further volcanic disaster risk management of Oro-Oro Ombo. According to the result the current map is going to reform the knowledge towards the recent hazards and risks.
Semi-supervised hierarchical ensemble clustering based on an innovative distance metric and constraint information

Agglomerative Hierarchical Clustering (AHC) is a bottom-up clustering strategy in which each object is originally a cluster, and more pairs of clusters are formed by traversing the hierarchy. It has been proven that there is no individual AHC clustering algorithm that can be efficient in all situations. In order to address this problem, ensemble clustering techniques have been introduced. These techniques combine the results of several output partitions to achieve a consensus with higher accuracy compared to an individual clustering algorithm. This paper proposes an AHC-based ensemble semi-supervised clustering algorithm to improve performance. In semi-supervised clustering, class membership information is used in some objects. Here, we introduce the Semi-Supervised Ensemble Hierarchical Clustering based on Constraints Information (SSEHCCI) algorithm. SSEHCCI is developed using several individual clustering algorithms based on AHC. SSEHCCI includes a flexible weighting policy to generate base partitions and uses the constraints information to configure the semi-supervised clustering. In addition, SSEHCCI uses an innovative distance measure to calculate the distance between each pair of objects. Experimental results show that SSEHCCI performs better than existing semi-supervised algorithms on some University of California Irvine (UCI) datasets. Specifically, we observed an average accuracy of SSEHCCI compared to SSDC and RSSC of 2.6% and 1.8%, respectively.
Designing Ising machines with higher order spin interactions and their application in solving combinatorial optimization

The Ising model provides a natural mapping for many computationally hard combinatorial optimization problems (COPs). Consequently, dynamical system-inspired computing models and hardware platforms that minimize the Ising Hamiltonian, have recently been proposed as a potential candidate for solving COPs, with the promise of significant performance benefit. However, prior work on designing dynamical systems as Ising machines has primarily considered quadratic interactions among the nodes. Dynamical systems and models considering higher order interactions among the Ising spins remain largely unexplored, particularly for applications in computing. Therefore, in this work, we propose Ising spin-based dynamical systems that consider higher order (> 2) interactions among the Ising spins, which subsequently, enables us to develop computational models to directly solve many COPs that entail such higher order interactions (i.e., COPs on hypergraphs). Specifically, we demonstrate our approach by developing dynamical systems to compute the solution for the Boolean NAE-K-SAT (K ≥ 4) problem as well as solve the Max-K-Cut of a hypergraph. Our work advances the potential of the physics-inspired 'toolbox' for solving COPs.
Perceived Importance of Metrics for Agile Scrum Environments

Metrics are key elements that can give us valuable information about the effectiveness of agile software development processes, particularly considering the Scrum environment. This study aims to learn about the metrics adopted to assess agile development processes and explore the impact of how the role performed by each member in Scrum contributed to increasing/reducing the perception of the importance of these metrics. The impact of years of experience in Scrum on this perception was also explored. To this end, a quantitative study was conducted with 191 Scrum professionals in companies based in Portugal. The results show that the Scrum role is not a determining factor, while individuals with more years of experience have a higher perception of the importance of metrics related to team performance. The same conclusion is observed for the business value metric of the product backlog and the percentage of test automation in the testing phase. The findings allow for extending the knowledge about Scrum project management processes and their teams, in addition to offering important insights into the implementation of metrics for software engineering companies that adopt Scrum.
Cooperative behavior in blockchain-based complementary currency networks through time: The Sarafu case study

The effort to reach the 17 Sustainable Development Goals by the United Nations has incentivized the adoption of IT solutions in many fields. Many systems for sustainable economic development are now relying on a digital form making them more accessible and provides the access to new functionalities. A very interesting example of such systems are complementary currencies i.e. cooperative currency systems that support national economies to provide humanitarian aid and promote sustainable development. While there are many studies on the principles and case studies of successful complementary currencies, many aspects are still unexplored, especially regarding cooperative behavior. Cooperative behavior in these systems is a key aspect, as complementary currencies are often born out of cooperation among members that face a period of crisis or they usually have the objective of creating bonds of reciprocity and integrating social networks between people, which should lead to increased cooperation. However, there is a lack of studies on many aspects of cooperative behavior in complementary currencies, such as how such behavior changes over time, especially in times of a crisis like the COVID-19 pandemic. Moreover how cooperation behavior is affected by time and different geographical locations is still unclear. In this work, we analyze Sarafu, a complementary currency that went digital and now relies on blockchain technology. Sarafu is a successful case of a complementary currency that was used for humanitarian aid during the COVID-19 pandemic. Moreover, Sarafu is a perfect case study for the study of cooperative behavior, as it implements a special type of account, the group account, to support cooperation groups. This feature supports the study of group dynamics and behavior. What we find is that Sarafu users exhibit strong reliance on cooperation groups; we also observe that the interaction of users and cooperation groups is influenced by both time and geographical location. The study of group accounts and in general mechanisms that promote cooperation can be useful for other humanitarian or community development projects. Moreover, similar cooperation enhancers could have an important role in other social development projects, and in general, in any setting where there is a strong need to foster cooperation for reaching social good.