Reviewerly/exHarmony huggingface dataset:
Geo-information-enabled village level micro-planning for enhancing common good

Engaging communities in village-level micro-planning encourages them to participate in the decision-making process of village development. While ample evidence exists of geospatial information-enabled district level micro-planning in India the same cannot be said for village level micro-planning. Local aspirations often drive village-level micro-plans owing to a lack of objective information. This study is conducted in two phases: i) deciphering crucial factors for village-level micro-planning aided by fuzzy cognitive maps, and ii) showcasing better village-level planning by the communities with access to geospatial information. Fuzzy cognitive map-based simulations revealed that access to information is the most crucial factor in the micro-planning process followed by the response system, finance, planning and execution of development works, community participation, and awareness. On receiving 16 thematic maps on Constitutionally delegated functions to local planning bodies, prepared with the aid of geographic information system (GIS), community representatives and village level local authorities identified the status of their village development vis-à-vis other villages. Showing competitive spirit for village development, they prepared better village level plans and end up creating more common goods after having modified the existing plans.
Towards detecting the level of trust in the skills of a virtual assistant from the user’s speech

Research has shown that trust is an essential aspect of human–computer interaction directly determining the degree to which the person is willing to use a system. An automatic prediction of the level of trust that a user has on a certain system could be used to attempt to correct potential distrust by having the system take relevant actions like, for example, apologizing or explaining its decisions. With this goal in mind, in this work we aim to explore the feasibility of automatically detecting the technical competence or ability of a virtual assistant (VA) from the user’s speech, a simple proxy for the task we truly care about: detecting whether the user trusts the ability of the VA. Since, to our knowledge, no public databases were available to perform such study, we developed a novel protocol for collecting speech data from subjects interacting with VAs with different skill levels. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a VA. At the beginning of each session, subjects are informed that the VA they are going to use has been previously rated by other users as being either competent or incompetent. During the session, the VA answers the subjects’ questions consistently to its alleged ability. All interactions are speech-based, with subjects and VAs communicating verbally, which allows the recording of speech produced under different conditions. The goal of the protocol was to induce subjects to either trust or distrust the VA’s skills, assuming that this would, in turn, affect their speech patterns in ways that could be automatically detected. Using this protocol, we collected a speech corpus in Argentine Spanish which is publicly available for research use. We show clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent’s skills. Using the collected data, we developed a system to detect the ability of the VA with which a subject interacted during a session, based on the subject’s speech patterns. We found that it was possible to detect whether the VA was competent or incompetent with an accuracy up to 76%, compared to a random baseline of 50%. Our analysis suggests that these results are possible because the subjects change the way they speak to the VA depending on whether they perceive it as more or less competent; that is, depending on whether they trust its ability or not.
Awareness requirement and performance management for adaptive systems: a survey

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools).
Exploring the role of conscientiousness on visualization-supported decision-making

Understanding how visualizations can support decision-making continues to be one of the most relevant challenges in current research. However, prior work provides limited knowledge regarding how the decision-maker profile and, in particular, psychological constructs affect decision-making. Weighing how conscientiousness affects one's tendency to follow the rules and prioritize tasks, this work explores if this personality trait plays a role in visualization-supported decision-making. We asked participants to perform a series of multi-attribute choices using visualizations of high-dimensional data. Further, we study if the quality feedback of the past choice affects the decision-making process. Our results suggest that the feedback quality of past decisions affects how much individuals invest in data analysis and decision-making. In addition, user confidence is affected by conscientiousness scores, resulting in conscientious individuals changing their choices more often. Our findings provide new insights into the research challenges of analyzing decision-making in a visualization setting.
Equivalent conditions for weak continuity of nonlinear filters

This paper studies weak continuity of nonlinear filters. It is well-known that Borel measurability of transition probabilities for problems with incomplete state observations is preserved when the original discrete-time process is replaced with the process whose states are belief probabilities. It is also known that the similar preservation may not hold for weak continuity of transition probabilities. In this paper we show that the sufficient condition for weak continuity of transition probabilities for beliefs introduced by Kara et al. (2019) is a necessary and sufficient condition for semi-uniform Feller continuity of transition probabilities. The property of semi-uniform Feller continuity was introduced recently by Feinberg et al. (2022), and the original transition probability for a Markov decision processes with incomplete information has this property if and only if the transition probability of the process, whose state is a pair consisting of the belief probability and observation, also has this property. Thus, this property implies weak continuity of nonlinear filters. This paper also reviews several necessary and sufficient conditions for semi-uniform Feller continuity.
A partition-based convergence framework for population-based optimization algorithms

Population-based optimization algorithms, such as genetic algorithm and particle swarm optimization, have become a class of important algorithms for solving global optimization problems. However, there is an issue that the global convergence is often absent for most of them. This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve this troubling problem. In this framework, regular partitions and evolutions of populations are implemented alternatively. Specifically, the initial population is generated from a regular partition on the search space; after several generations of evolution of the population, the evolution result is returned to join in the regular partition again, and a new population is generated. Repeat such progress until some stop condition is satisfied. Global convergence is guaranteed for the framework. Then this convergence framework is applied to particle swarm optimization, differential evolution, and genetic algorithm. The modified algorithms are globally convergent and perform better than the original version.
A stochastic approximation approach to fixed instance selection

Instance selection plays a critical role in enhancing the efficacy and efficiency of machine learning tools when utilised for a data mining task. This study proposes a fixed instance selection algorithm based on simultaneous perturbation stochastic approximation that works in conjunction with any supervised machine learning method and any corresponding performance metric, which we call SpFixedIS. This algorithm provides an approximate solution to the NP-hard instance selection problem and additionally serves as a way of intelligently selecting a specified number of instances within a training set with regards to a machine learning model. The shape of the objective function obtained from the test accuracy against the number of instances selected is examined extensively for our instance selection algorithm. The SpFixedIS algorithm was tested on 43 diverse datasets across 6 different machine learning classifiers. The results show that in over 90% of cases SpFixedIS provides a statistically significant improvement at a 5% level with intelligent selection over random selection for the same number of instances. Furthermore, with respect to probabilistic models, specifically Gaussian Naive Bayes, SpFixedIS provides a statistically significant improvement compared to models that utilise the entirety of the training set in 84% of the experimented values ranging from 50 to 1000 instances.
Detection and Tracking Method of Small-Sized UAV Based on YOLOv5

In recent years, accidents such as casualties and economic losses have occurred due to unmanned aerial vehicles (UAVs) flying in violation of regulations. The monitoring and countermeasures of UAVs with low flying altitude, slow flying speed and small size have become the current research hotspot. Vision-based methods are still one of the most mainstream methods, but due to the characteristics of UAVs such as small size, large attitude changes, and low flight altitudes, there are difficulties such as poor imaging contrast, complex background, and small proportion of targets. Aiming at the above difficulties, this paper proposes an improved YOLOv5 UAV detection algorithm and tracking method. The detection probability of drones is improved by adding a detection heads and attention module, and high-speed tracking performance is achieved by training a low-resolution detector combined with the Kalman algorithm. This paper deploys this method on NVIDIA Jetson Xavier NX, resulting in an output rate of 200 FPS.
Focus on Objects: An Image Super-Resolution Model for Region of Interest

Image super-resolution (SR) can reconstruct a low-resolution (LR) image into a high-resolution (HR) image to provide an excellent visual experience. However, this task is pixel-wise, and for images with an ordinary resolution, its processing speed is inefficient, which is challenging to meet the needs of practical applications. People only focus on local regions in the image and step back to super-resolve these regions of interest, which can significantly reduce the computational complexity. We propose an image SR model that only focuses on the region of interest in one image, effectively uses people’s visual attention and significantly reduces computing power consumption.
Contrastive Semantic Similarity Learning for Multi-Hop Question Answering over Event-Centric Knowledge Graphs

Question answering in natural languages provides an intuitive and efficient way to help people access the rich information stored in various kinds of knowledge graphs (KGs). One of the key challenges for question answering over knowledge graphs (KGQA) is to learn a semantic representation of the input question and candidate relation chains over KGs and accurately measure the similarity between them. However, existing methods often failed to capture the semantic similarity for complex question answering, e.g., multi-hop and temporal constrained situations. In addition, existing KGQA related research mostly concentrates on entities while often ignores the events which contain a large portion of the world knowledge. To solve this issue, we propose a Contrastive Semantic Similarity Learning (CSSL) method for multi-hop question answering over event-centric KGs. In this method, for candidate relation chains generation, the retrieval subgraph is first constructed by identifying the topic event or entity in the question. To better accommodate complex questions, we introduce the contrastive learning framework to learn a common semantic space, where the similarity score is finally calculated to select the final answer. The experimental results on the EventQA dataset show that the proposed method achieves superior performances compared to the state-of-the-art baselines.
Semantic Orientation of Crosslingual Sentiments: Employment of Lexicon and Dictionaries

Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on the Twitter dataset and the robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively.
Linguistic Steganalysis by Enhancing and Integrating Local and Global Features

With the improvement of steganography, the difference of statistical distribution caused by information hiding is getting smaller and smaller, increasing the difficulty of steganalysis. The existing steganalysis models value different features equally, while the differences in the importance of high-dimensional features are ignored. That is the influences of feature quality on model performance are not considered. To fill the gap, a novel linguistic steganalysis by enhancing and integrating local and global features is proposed in this letter. It extracts and integrates the features of two dimensions, namely local semantic features and global long-term dependencies, to construct a joint feature map. Then, to improve the quality of features, a group-wise enhancement mechanism is employed, which divides features into multiple groups, enhancing important features in each group while weakening the less important ones by generating an important coefficient for each sub-feature in each semantic group. Finally, the enhanced features are used to further extract high-quality text representation to help the classification module distinguish cover and stego texts more accurately. The experimental results show that the proposed model has better detection performance in multiple hidden scenarios. The ablation experiment manifests that using integrated and enhanced features to extract high-quality text representations can effectively improve the discrimination ability of the model.
Quantum algorithm for matrix logarithm by integral formula

In scientific computing, one can find a wide application of the matrix-vector product f(A)b. Recently, a quantum algorithm that computes the state $$|f\rangle $$ corresponding to f(A)b has been proposed in Takahira et al. (Quantum Inf Comput 20(1/2):14–36, 2020). However, this important algorithm can not be directly applied to the matrix logarithm, which is one of the significant matrix functions. In this paper, we propose an original quantum algorithm to compute the state $$|f\rangle = \log (A)|b\rangle / \Vert \log (A)|b\rangle \Vert $$ , via the integral representation of $$\log (A)$$ and the Gauss–Legendre quadrature rule, using LCU method and block-encoding technique as subroutines.
Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement

Recently, recommender systems based on knowledge graphs (KGs) have become a popular research direction. Graph neural network (GNN) is the key technology of KG-based recommendation systems. However, existing GNNs have a significant flaw: They cannot explicitly model users’ intent in recommendations. Intent plays an essential role in users’ behaviors. For example, users may first generate an intent to purchase a certain group of items and then select a specific item from the group based on their preferences. Therefore, explicitly modeling intent has a positive significance for improving recommendation performance and providing explanations for recommendations. In this article, we propose a new model called Topic-aware Intention Network (TIN) for explainable recommendations with KGs. TIN models user representations from both preference and intent views. Specifically, we design a relational attention graph neural network to selectively aggregate information in KG to learn user preferences, and we propose a knowledge-enhanced topic model to learn user intent, which is viewed as topics hidden in user behavior sequences. Finally, we obtain the user representation by fusing user preference and intent through an attention network. The experimental results show that our proposed model outperforms the state-of-the-art methods and can generate reasonable explanations for the recommendation results.
Hyper Parameter Optimization of CRNN for Printed Devanagari Script Recognition using Taguchi's Method

The Devanagari script is one of the most widely used scripts worldwide. The existing deep learning-based optical character recognition system for printed Devanagari scripts using Convolutional Neural Network – Recurrent Neural Network , or CRNN is not robust enough to recognize any randomly printed Devanagari scanned document. At present, the hyper-parameters of the CRNN system are selected randomly either with the trial-and-error or grid search methods. Moreover, there is no optimized way to choose the hyper-parameters of the CRNN, which improves the recognition accuracy for Devanagari documents. Furthermore, the lack of standard Devanagari script datasets has hampered the development of word recognizers. In this paper, the hyper-parameter of the CRNN system is optimized using Taguchi's method of optimization. The performance of the hyper-parameters optimized CRNN system is compared with the current state-of-the-art text recognition CRNN network. The results reveal that the CRNN optimized with Taguchi's method performs better than the CRNN-based systems.
Factors Affecting the Use of Blockchain Technology in Humanitarian Supply Chain: A Novel Fuzzy Large-Scale Group-DEMATEL

Based on previous evidence, the use of blockchain for improving Supply Chains (SCs) regarding humanitarian projects has received attention over the past five years. The present study is innovative in investigating crucial parameters affecting the using of Blockchain Technology (BT) in Humanitarian Supply Chains (HSCs). More precisely, this study emphasizes parameters that affect blockchain in the HSCs and presents a new fuzzy large-scale group decision-making trial and evaluation laboratory (fuzzy large-scale group-DEMATEL) approach to analyze the interdependence of contributing factors for using BT in HSCs. This method consists of two stages: (1) clustering the large-scale group-experts into small subgroups by their characteristics, and (2) identifying the key factors affecting BT in HSCs with a novel fuzzy large-scale group-DEMATEL approach. According to experts, in this study, among the 25 evaluated factors, disintermediation has been identified as the most important one, followed by anonymity and security. A closer look reveals that 13 and 12 factors have been "cause" and "effect" factors, respectively. Our research can be used to promote the effectiveness of using BT in HSCs, so as to promote the proper distribution of relief materials in practical disasters.
Design and Implementation of Immersive Display Interactive System Based on New Virtual Reality Development Platform

In order to solve the single form of traditional museums, reduce the impact of space and time on ice and snow culture and expand the influence and dissemination of ice and snow culture, we developed an immersive Winter Olympics virtual museum based on Unreal Engine 4. We used 3ds Max to build virtual venues, import Unreal Engine 4 through Datasmith, and explore the impact of lighting, materials, sound, and interaction on virtual museums. This article gives users an immersive experience by increasing the realism of the space, which provides a reference for the development of virtual museums.
Filtering and Extended Vocabulary based Translation for Low-resource Language Pair of Sanskrit-Hindi

Neural Machine Translation (NMT) is widely employed for language translation tasks because it performs better than the conventional statistical and phrase-based approaches. However, NMT techniques involve challenges, such as requiring a large and clean corpus of parallel data and the inability to deal with rare words. They need to be faster for real-time applications. More work needs to be done using NMT to address the challenges in translating Sanskrit, one of the oldest and rich languages known to the world, with its morphological richness and limited multilingual parallel corpus. There is usually no similar data between a language pair; hence, no application exists so far that can translate Sanskrit to/from other languages. This study presents an in-depth analysis to address these challenges with the help of a low-resource Sanskrit-Hindi language pair. We employ a novel training corpus filtering with extended vocabulary in a zero-shot transformer architecture. The structure of the Sanskrit language is thoroughly investigated to justify the use of each step. Furthermore, the proposed method is analyzed based on variations in sentence length and also applied to a high-resource language pair in order to demonstrate its efficacy.