Reviewerly/exHarmony huggingface dataset:
An Opinion Mining Approach to Handle Perspectivism and Ambiguity: Moving Toward Neutrosophic Logic

One main incentive for the utilization of opinion mining (OM) in social media is the impossibility of manually analyzing millions of opinions.However, applying OM for decision-making requires imitating human brain reasoning for more pragmatic results.Analyzing opinions in human-like intelligence is vital for avoiding misleading results.This occurs when the process can properly excogitate social influence and environmental uncertainty.In this study, an OM model for Twitter is proposed to handle perspectivism and opinion ambiguity.For perspectivism, social network analysis (SNA) is conducted, where users are ranked and then weighted using the UCINET tool and neural networks.An uncertainty classifier is used to integrate users' influence levels with the polarity scores of their texts, providing a new polarity score that can reflect the real-world reasoning of opinions.Polarity scores needed for integration is done using the lexicon resource TextBlob.In the proposed model, three uncertainty classifiers are tested: type1 fuzzy logic (T1-FL), type2 fuzzy logic (T2-FL), and neutrosophic logic (NL).A comparative analysis of the methods shows the ability of NL to deal with the uncertainty existing in the data more accurately, proving the benefit of NL in improving the power of OM in social media.
Encouraging Elderly Self-care by Integrating Speech Dialogue Agent and Wearable Device

Currently, the world's population has been aging. Especially, Japan has entered a super-aging society. We develop a listening service using a spoken dialogue agent as a system to support self-help for the in-home elderly in our research group. However, it is impossible to promote self-care for physical and mental illnesses that the elderly are not aware of. The purpose of this paper is to encourage more self-care among the elderly. The key idea is to make the elderly aware of the need for care by adding health data to the agent's topics of conversation, not just the content of past utterances. As an approach, we link wearable devices with interactive agents. The proposed system is expected to make the elderly aware of their physical and mental discomforts, which they are not aware of themselves, and to promote self-care.
Vision-Based Continuous Sign Language Spotting Using Gaussian Hidden Markov Model

The presence of movement epenthesis ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</i> ) between two consecutive signs in a continuous sign language makes sign language recognition (SLR) a challenging task. In this letter, we propose a vision-based continuous SL spotting system, which separates the meaningful signs from the sign sequences by removing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</i> components from H.264/AVC compressed videos. The work is based on a two-state hidden Markov model (HMM) with Gaussian emission probability. The HMM is trained by the feature set extracted from the entire sign sequence video and finally, the hidden state-sequence is decoded using the Viterbi algorithm. From the decoded state-sequence, the sign spotting is done. The feature set comprises features extracted from the compressed domain as well as uncompressed domain analysis of the sign video. The video database is composed of American SL videos collected from Boston University database. From the experimental results, it is seen that the proposed system can spot the sign and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</i> frames with a spotting rate of about 83%.
A Scalable Emulator for Quantum Fourier Transform Using Multiple-FPGAs With High-Bandwidth-Memory

Quantum computing is regarded as the future of computing that hopefully provides exponentially large processing power compared to the conventional digital computing.However, current quantum computers do not have the capability to correct errors caused by environmental noise, so that it is difficult to run useful algorithms that require deep quantum circuits.Therefore, emulation of quantum circuits in digital computers is essential.However, emulation of large quantum circuits requires enormous amount of computations, and leads to a very large processing time.To reduce the processing time, we propose an FPGA emulator with high-bandwidth-memory to emulate quantum Fourier transform (QFT), which is a major part of many quantum algorithms.The proposed FPGA emulator is scalable in terms of both processing speed and the number of qubits, and extendable to multiple FPGAs.We performed QFT emulations up to 30 qubits using two FPGAs.According to the measured results, we have achieved 23.6 ∼ 24.5 times speed-up compared to a fully optimized 24-core CPU emulator.
Batch-OT with Optimal Rate

We show that it is possible to perform n independent copies of 1-out-of-2 oblivious transfer in two messages, where the communication complexity of the receiver and sender (each) is $$n(1+o(1))$$ for sufficiently large n. Note that this matches the information-theoretic lower bound. Prior to this work, this was only achievable by using the heavy machinery of rate-1 fully homomorphic encryption (Rate-1 FHE, Brakerski et al., TCC 2019). To achieve rate-1 both on the receiver's and sender's end, we use the LPN assumption, with slightly sub-constant noise rate $$1/m^{\epsilon }$$ for any $$\epsilon >0$$ together with either the DDH, QR or LWE assumptions. In terms of efficiency, our protocols only rely on linear homomorphism, as opposed to the FHE-based solution which inherently requires an expensive "bootstrapping" operation. We believe that in terms of efficiency we compare favorably to existing batch-OT protocols, while achieving superior communication complexity. We show similar results for Oblivious Linear Evaluation (OLE). For our DDH-based solution we develop a new technique that may be of independent interest. We show that it is possible to "emulate" the binary group $$\mathbb {Z}_2$$ (or any other small-order group) inside a prime-order group $$\mathbb {Z}_p$$ in a function-private manner. That is, $$\mathbb {Z}_2$$ operations are mapped to $$\mathbb {Z}_p$$ operations such that the outcome of the latter do not reveal additional information beyond the $$\mathbb {Z}_2$$ outcome. Our encoding technique uses the discrete Gaussian distribution, which to our knowledge was not done before in the context of DDH.
E-learning for transportation professionals (post graduate program) during the COVID-19 pandemic

Transportation industry prosperity is closely connected with training of transportation professionals at all level of their education and in various economic and epidemiological situations. The study aimed to show the reactions of 1st year post graduate research students (transportation professionals) on key issues of e-learning during post-extreme period (October 2020-January 2021). To answer research questions connected with characteristics of post-graduate students’ reactions to remote e-learning, risks for the students and searching novel e-learning models, the questionnaire and a descriptive approach for open-ended and close-ended questions analysis, semantic method for assessing the language content obtained empirically, the quantitative-descriptive method for presenting the research results were used. 33 transportation professionals (Engineering Field, IT, Social Science and Humanities) took part in the survey. Face-to-face format of learning was preferred by 30.30 % students (“T” and “SH” respondents). Remote e-learning only was satisfied for 39.39 % students (“T” and “IT”). At that time desire to keep such e-learning experience was demonstrated by 48.48 % of the students. They were ready for e-learning returning. 39.39 % of all the students, except 3.03 % (“SH”) were ready to remote e-learning activity “when it is necessary”. E-learning risks were mainly associated with social adaptation in the digital learning environment. At the same time e-learning was pointed out as a chance for sustainable scientific activity.
Knowledge tracing for adaptive learning in a metacognitive tutor

Abstract Adaptive learning is provided in intelligent tutoring systems (ITS) to enable learners with varying abilities to meet their expected learning outcomes. Despite the personalized learning afforded by ITSes using adaptive learning, learners are still susceptible to shallow learning. Introducing metacognitive tutoring to teach learners how to be aware of their knowledge can enable deeper learning. However, metacognitive tutoring on top of cognitive tutoring can lead to unsustainable cognitive loads. Using metacognitive inputs for knowledge tracing was explored for managing cognitive loads. Hidden Markov models (HMM) and artificial neural networks were used to train models on a synthetic dataset created from predetermined learner personas. The models created with metacognitive inputs were compared with the models created without said inputs. The models using metacognitive inputs performed better than the standard models while still following learning intuitions. This indicates that combining knowledge tracing and metacognitive tutoring is a viable option for improving learning outcomes. This is an important finding since online learning, which demands metacognitive skills, is becoming popular for various topics, including those that are challenging even with immediate teacher assistance.
Multi-Scale Network with Integrated Attention Unit for Crowd Counting

Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering. Moreover, since the crowd images in this case can range from low density to high density, detection-based approaches are hard to apply for crowd counting. Recently, deep learning-based regression has become the prominent approach for crowd counting problems, where a density-map is estimated, and its integral is further computed to acquire the final count result. In this paper, we put forward a novel multi-scale network (named 2U-Net) for crowd counting in sparse and dense scenarios. The proposed framework, which employs the U-Net architecture, is straightforward to implement, computationally efficient, and has single-step training. Unpooling layers are used to retrieve the pooling layers’ erased information and learn hierarchically pixel-wise spatial representation. This helps in obtaining feature values, retaining spatial locations, and maximizing data integrity to avoid data loss. In addition, a modified attention unit is introduced and integrated into the proposed 2U-Net model to focus on specific crowd areas. The proposed model concentrates on balancing the number of model parameters, model size, computational cost, and counting accuracy compared with other works, which may involve acquiring one criterion at the expense of other constraints. Experiments on five challenging datasets for density estimation and crowd counting have shown that the proposed model is very effective and outperforms comparable mainstream models. Moreover, it counts very well in both sparse and congested crowd scenes. The 2U-Net model has the lowest MAE in both parts (Part A and Part B) of the ShanghaiTech, UCSD, and Mall benchmarks, with 63.3, 7.4, 1.5, and 1.6, respectively. Furthermore, it obtains the lowest MSE in the ShanghaiTech-Part B, UCSD, and Mall benchmarks with 12.0, 1.9, and 2.1, respectively.
A Secure Data Sharing Scheme Based on CP-ABE in VANETs

Vehicle ad-hoc networks (VANETs), as the special mobile ad hoc network (MANET), are able to realize the rapid interconnection among vehicles and roadside infrastructures, so as to guarantee that vehicles obtain continuous and stable network services. As one of the vital services supported by VANETs, data sharing service supports vehicles to gain interested data information (e.g. road condition information) in time and improve the driver’s experience. However, due to the wireless network communication environment, it is easy for adversaries to forge sharing data to confuse the surrounding vehicles, or invade the privacy of the drivers by collecting the data shared from the surrounding vehicles, which may mislead drivers or even cause serious traffic accidents. This paper proposes a secure data sharing scheme in VANETs. By integrating ciphertext policy attribute-based encryption mechanism (CP-ABE), the privacy of the data sender is protected and the data receiver is able to obtain reliable sharing data in time. Analysis results show that the security and performance of the scheme are improved compared with the traditional ones.
Ranking Requirements Using MoSCoW Methodology in Practice

Requirement prioritization is performed in order to analyze business requirements and to define the required capabilities leading to potential solutions that will fulfill stakeholder needs. During the analysis, the needs and informal concerns are transformed into formal solution requirements describing the behavior of solution components in details. The developed models can describe the current state of the organization and are used for validating the solution scope among mangers and stakeholders. This facilitates identification of open opportunities for improvement or assists stakeholders in understanding the current state. Several techniques have been applied for requirements prioritization in a case study of the conventional commercial bank where the main problems of the communication management process have been formulated and illustrated by the fishbone diagram. The MoSCoW technique has been applied to identification of four requirement groups, whose impact on the results principally differ within the scope of the identified problems. The obtained list of prioritized requirements should be used on next project stages since it will be exploited by the managers during their planning future jobs on the solution implementation. The paper results are aimed at helping the stakeholders develop a common point of view on the strategic goals of the project.
Thinking Inside Uncertainty: Interest Moment Perception for Diverse Temporal Grounding

Given a language query, temporal grounding task is to localize temporal boundaries of the described event in an untrimmed video. There is a long-standing challenge that multiple moments may be associated with one same video-query pair, termed label uncertainty. However, existing methods struggle to localize diverse moments due to the lack of multi-label annotations. In this paper, we propose a novel Diverse Temporal Grounding framework (DTG) to achieve diverse moment localization with only single-label annotations. By delving into the label uncertainty, we find the diverse moments retrieved tend to involve similar actions/objects, driving us to perceive these interest moments. Specifically, we construct soft multi-label through semantic similarity of multiple video-query pairs. These soft labels reveal whether multiple moments in the intra-videos contain similar verbs/nouns, thereby guiding interest moment generation. Meanwhile, we put forward a diverse moment regression network (DMRNet) to achieve multiple predictions in a single pass, where plausible moments are dynamically picked out from the interest moments for joint optimization. Moreover, we introduce new metrics that better reveal multi-output performance. Extensive experiments conducted on Charades-STA and ActivityNet Captions show that our method achieves state-of-the-art performance in terms of both standard and new metrics.
Fine-Grained Benchmarking and Targeted Optimization: Enabling Green IoT-Oriented Blockchain in the 6G Era

For 6G-empowered IoT, blockchain is widely regarded as a fundamental technique for addressing the security and privacy issues. Nonetheless, the current blockchains suffer from several flaws, especially the low performance and scalability. They seriously hinder the blockchain deployments in 6G-empowered IoT with high concurrency and complexity. To address this problem, we need to better understand blockchains and try to optimize them. However, we still lack a standard tool for comprehensively benchmarking the IoT-oriented blockchains in 6G era. As to blockchain optimization, the current general-purpose strategies can hardly hold efficiency all the time due to the heterogeneity of 6G-empowered IoT. In this paper, we present a novel pipeline for facilitating the green IoT-oriented blockchains in 6G era, called “fine-grained benchmarking + targeted optimization.” Specifically, we first design a benchmarking framework which realizes the whole workflow of data acquisition, log parsing, and metric visualization, offering quantitative blockchain performance reports. Moreover, our framework provides ten metrics divided into four dimensions, leading blockchain benchmarking to the fine-grained level. Based on the acquired insights, we further present the concept of targeted optimization, i.e., directly designing optimization strategies based on the metrics that perform poorly in benchmarking. To better illustrate this pipeline, we take IOTA, the most potential IoT-oriented blockchain in 6G era, as a case study. We observe two defects of IOTA, i.e., low power efficiency and slow ledger synchronization. Following our pipeline, we design two targeted optimization mechanisms, namely Proof-of-Resource Efficiency and Dynamic Local Snapshot Protocol. Extensive experiments demonstrate that our pipeline can maximize the optimization efficiency, successfully addressing the issues hindering IOTA. Last but not least, we emphasize that the proposed pipeline can also be applied by other IoT-oriented blockchains in the 6G era.
Property-Preserving Hash Functions for Hamming Distance from Standard Assumptions

Property-preserving hash functions allow for compressing long inputs $$x_0$$ and $$x_1$$ into short hashes $$h(x_0)$$ and $$h(x_1)$$ in a manner that allows for computing a predicate $$P(x_0, x_1)$$ given only the two hash values without having access to the original data. Such hash functions are said to be adversarially robust if an adversary that gets to pick $$x_0$$ and $$x_1$$ after the hash function has been sampled, cannot find inputs for which the predicate evaluated on the hash values outputs the incorrect result. In this work we construct robust property-preserving hash functions for the hamming-distance predicate which distinguishes inputs with a hamming distance at least some threshold t from those with distance less than t. The security of the construction is based on standard lattice hardness assumptions. Our construction has several advantages over the best known previous construction by Fleischhacker and Simkin (Eurocrypt 2021). Our construction relies on a single well-studied hardness assumption from lattice cryptography whereas the previous work relied on a newly introduced family of computational hardness assumptions. In terms of computational effort, our construction only requires a small number of modular additions per input bit, whereas the work of Fleischhacker and Simkin required several exponentiations per bit as well as the interpolation and evaluation of high-degree polynomials over large fields. An additional benefit of our construction is that the description of the hash function can be compressed to $$\lambda $$ bits assuming a random oracle. Previous work has descriptions of length $$\mathcal {O}({\ell \lambda })$$ bits for input bit-length $$\ell $$ . We prove a lower bound on the output size of any property-preserving hash function for the hamming distance predicate. The bound shows that the size of our hash value is not far from optimal.
Applying and Researching DevOps: A Tertiary Study

DevOps is an emerging software development methodology, that differs from more traditional approaches due to the closer involvement of the customer and the adoption of "continuous-*" (e.g., integration, deployment, delivery, etc.) practices.The vast research on DevOps (including numerous secondary studies) published in a short timeframe, and the diversity of the authors' research backgrounds (e.g., from a Dev or an Ops perspective), has inevitably produced a long list of investigated topics, which use inconsistent terminology.The goal of this study is to analyze literature reviews on DevOps with respect to: (a) the research topics in DevOps; (b) the terms that are mapped to each topic; and (c) the consistency of terminology.To achieve this goal, we have performed a tertiary study, i.e., a systematic mapping study that uses as primary studies "Systematic Literature Reviews" and "Mapping Studies".For Data Extraction, Analysis, and Synthesis (DEAS) we propose a novel approach relying on thematic analysis, statistical analysis, and meta-analysis.The results unveiled 7 core topics on DevOps research, out of which DevOps features and DevOps practices are dominant ones.Additionally, as expected various terminology ambiguities have been identified, most between features as practices, as well as, between challenges faced before adopting DevOps and while applying DevOps.The main contribution of this study is the disambiguation of the mapping of terms to topics.Along this process we highlight both inconsistencies-attempting to resolves ambiguities, as well as topics and terms with high levels of consistency; aiding researchers and practitioners.
Explainable Clinical Decision Support: Towards Patient-Facing Explanations for Education and Long-Term Behavior Change

There is an increasing shift towards the self-management of long-term chronic illness by patients in a home setting, supported by personal health electronic equipment. Among others, self-management requires comprehensive education on the illness, i.e., understanding the effects of nutritional, fitness, and medication choices on personal health; and long-term health behavior change, i.e., modifying unhealthy lifestyles that contribute to chronic illness. Smart health recommendations, generated using AI-based Clinical Decision Support (CDS), can guide patients towards positive nutritional, fitness, and health behavioral choices. Moreover, we posit that explaining these recommendations to patients, using Explainable AI (XAI) techniques, will effect education and positive behavior change. We present our work towards an explanation framework for rule-based CDS, called EXPLAIN (EXPLanations of AI In N3), which aims to generate human-readable, patient-facing explanations.
Handling Disagreement in Hate Speech Modelling

Abstract Hate speech annotation for training machine learning models is an inherently ambiguous and subjective task. In this paper, we adopt a perspectivist approach to data annotation, model training and evaluation for hate speech classification. We first focus on the annotation process and argue that it drastically influences the final data quality. We then present three large hate speech datasets that incorporate annotator disagreement and use them to train and evaluate machine learning models. As the main point, we propose to evaluate machine learning models through the lens of disagreement by applying proper performance measures to evaluate both annotators’ agreement and models’ quality. We further argue that annotator agreement poses intrinsic limits to the performance achievable by models. When comparing models and annotators, we observed that they achieve consistent levels of agreement across datasets. We reflect upon our results and propose some methodological and ethical considerations that can stimulate the ongoing discussion on hate speech modelling and classification with disagreement.