Reviewerly/exHarmony huggingface dataset:
Evaluation of Trust in Computer-Computed Results

The paper addresses the problem of evaluating the trust in the results of complex computer data analysis. The approach of constructing empirical dependencies based on similarity of precedents in the training sample, which has already become classical, is used. Two types of approximation of the causal bases have been constructed. The first type of approximation uses the functions of causal influence and, when there is only one cause, provides a confident interpretation of the results of computer analysis. The second type of approximation is based on simulation of the training data by random sampling from an unknown distribution. Both approaches implement approximate causal analysis and have advantages and disadvantages.
Explainable Modeling in Digital Twin

Stakeholders&#x0027; participation in the modeling process is important to successful Digital Twin (DT) implementation. The key question in the modeling process is to decide which options to include. Explaining the key question clearly ensures the organizations and end-users know what the digital models in DT are capable of. To support successful DT implementation, we propose a framework of explainable modeling to enable the collaboration and interaction between modelers and stakeholders. We formulate the modeling process mathematically and develop three types of automatically generated explanations to support understanding and build trust. We introduce three explainability scores to measure the value of explainable modeling. We illustrate how the proposed explainable modeling works by a case study on developing and implementing a DT factory. The explainable modeling increases communication efficiency and builds trust by clearly expressing the model competencies, answering key questions in modeling automatically, and enabling consistent understanding of the model.
Expansion to the Motion Sickness Susceptibility Questionnaire-Short Form: A Cross-Sectional Study

The primary objective of this study is to investigate the necessity of questioning virtual reality systems in the motion sickness susceptibility questionnaire (MSSQ)-short form. The secondary objective of this study is to determine the validity and reliability of the Turkish version of the MSSQ-short form, with proven validity and reliability.In the questionnaire form, for which expert opinion was obtained to maintain linguistic equivalence, the virtual reality items were added to the questionnaire. The questionnaire was then administered to 297 individuals. The results were statistically analyzed with and without these virtual reality items for validity and reliability.After the addition of the virtual reality items, the reliability of the questionnaire was found to be quite high (Cronbach's alpha r=0.912). The norm values between the original MSSQ-short form (12.9±9.9) and the Turkish MSSQ-short form (13.8±12.9) were found to be consistent.Motion sickness symptoms can occur not only during movement, but also with indirect stimulus. Our findings suggest that adding virtual reality items to the original form is important in long term practical applications. Our results show that the Turkish version of the original questionnaire is quite reliable. Submission of the MSSQ-short form in Turkish will be useful for documentation and will also encourage further research in this area.
A Scalable Deep Learning-Based Approach for Anomaly Detection in Semiconductor Manufacturing

The diffusion of the Industry 4.0 paradigm lead to the creation and collection of huge manufacturing datasets; such datasets contain for example measurements coming from physical sensors located in different equipment or even in different productive manufacturing organizations. Such large and heterogeneous datasets represent a challenge when aiming for developing data-driven approaches like Anomaly Detection or Predictive Maintenance. In this work we present a new approach for performing Anomaly Detection that is able to handle heterogeneous data coming from different equipment, work centers or production sites. The proposed approach exploits Deep Learning architectures: Autoencoders are employed to derive a &#x0027;normal&#x2019; behaviour of the system under exam that is then used for comparison when monitoring new data. The main strength of the proposed approach is its generality and scalability: the effectiveness of the proposed approach is demonstrated through experiments performed on real world data extracted from different workcenters in semiconductor manufacturing facilities.
Fuzzy-System Kernel Machines: A Kernel Method Based on the Connections Between Fuzzy Inference Systems and Kernel Machines

This article introduces the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy-system kernel machines</i> —a class of machine learning models based on the connection between fuzzy inference systems and kernel machines. For the connection, we observed a relationship between the representer theorem of kernel methods and the functional representation of nonsingleton fuzzy systems. We found that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonsingleton kernel on fuzzy sets</i> —a kernel defined in this article—is the core element allowing this two-way connection perspective. Consequently, a fuzzy system trained with the kernel method can be regarded as a kernel machine, whereas a kernel machine trained with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonsingleton kernel on fuzzy sets</i> can be interpreted as a fuzzy system. We conducted several experiments in supervised classification to understand the generalization power and properties of the proposed fuzzy-system kernel machines.
Corpus tools for parallel corpora of theatre plays: an introduction to TAligner and ACM-theatre

Software tools are of vital importance in corpus-based research, but they can also lead to restrictions on the type of supported corpora and the range of analyses that can be performed. For example, corpus analysis tools, as general purpose software, do not include specific features to process corpora of theatre plays. This situation is even worse for parallel corpora of theatrical texts, in that there is currently a lack of software that allows for both the alignment and analysis of parallel corpora here. In this contribution, we will first outline the peculiarities of theatre texts and suggest three software features to address them: annotation of the structural units of plays, alignment at the utterance level, and concordances and statistics using the annotated units. Second, we will present the specific functionalities of TAligner and ACM to build and analyse parallel corpora of play texts, showing how new avenues of research are opening up with the development of these tools.
Real-Time Activities of Daily Living Recognition Under Long-Tailed Class Distribution

Real-time/online Activities of Daily Living (ADL) recognition is an enabling technology for various ubiquitous computing applications. Most of the existing ADL recognition systems consider class balanced data, while the real-life datasets are significantly imbalanced because they follow the long-tailed class distribution. Such imbalanced training data can result in a biased system optimized to favor the majority classes while failing to recognize the minority classes. This paper proposes an online system that recognizes the ADL while considering the long-tailed class distribution. The system first generates hand-crafted and high-level features by using conventional learning and deep learning, which cover the advantage of both technologies to recognize the ADL. Next, the system uses an ensemble technique to concatenate the generated features. Finally, the system minimizes a loss function, which is a linear combination of focal loss for addressing the long-tailed class distribution problem and center loss for enhancing the discriminative power of the deeply learned features. We conduct several experiments on real-life long-tailed datasets to verify the accuracy of the system.
An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

The Human-Centered Internet of Things (HC-IoT) is fast becoming a hotbed of security and privacy concerns. Two users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key agreement. Most of the early authenticated key agreement systems relied on pairing, hashing, or modular exponentiation processes that are computationally intensive and cost-prohibitive. In order to address this problem, this paper offers a new three-party authenticated key agreement technique based on fractional chaotic maps. The new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password table. The projected security scheme utilized a hash function, which works well for the resource-limited HC-IoT architectures. Test results show that our new technique is resistant to password guessing attacks since it does not use a password. Furthermore, our approach provides users with comprehensive privacy protection, ensuring that a user forgery attack causes no harm. Finally, our new technique offers better security features than the techniques currently available in the literature.
A Simplex Method-Based Bacterial Colony Optimization for Data Clustering

The task of data clustering is to distribute data objects into groups based on similarity. In this paper, a simplex method is applied to improve bacterial colony optimization (BCO) searching capacity (SMBCO). To solve the data clustering problem, the proposed SMBCO algorithm is used. The superiority proposed SMBCO method is assessed using five machine learning datasets. The clustering algorithm's results are assessed with objective value and computational time. The results of experiments indicated that the SMBCO model attains high precision when compared with a conventional algorithm with faster convergence.
A categorical framework for congruence of applicative bisimilarity in higher-order languages

Applicative bisimilarity is a coinductive characterisation of observational equivalence in call-by-name lambda-calculus, introduced by Abramsky (1990). Howe (1996) gave a direct proof that it is a congruence, and generalised the result to all languages complying with a suitable format. We propose a categorical framework for specifying operational semantics, in which we prove that (an abstract analogue of) applicative bisimilarity is automatically a congruence. Example instances include standard applicative bisimilarity in call-by-name, call-by-value, and call-by-name non-deterministic $\lambda$-calculus, and more generally all languages complying with a variant of Howe's format.
Does Universities’ Research Output Aligned to National Development Goals Impact Economic Productivity? Evidence from Kenya

Few studies on developing countries have investigated the alignment of research output to a country’s development agenda and economic productivity. Using evidence from Kenya, this study sought to empirically determine whether the country’s research output is aligned to its development agenda in the first instance and to establish the output’s relationship to economic productivity. Journal publications were used to measure research output. From the analysis, 86% of the publications fell within one or more of the national development priority areas, though 60% were in only 6 of the 35 areas. Several areas had no publications at all during the period under investigation. Furthermore, excluding the health and education sectors, a strong positive relationship was established between the number of publications in different priority areas and those areas’ contribution to Gross Domestic Product. The Government, therefore, needs to avail research funding to research institutions, which, in turn, need to focus their research effort on all identified national development priority areas if Kenya’s development aspirations are to be achieved and the desired economic growth attained.
Emotional analysis of safeness and risk perception of London and Rome airports during the COVID-19 pandemic

The evaluation of perceived safeness and risk by individuals is really useful for security and safety managing. Every individual is founded on the opinion of other individuals to get a selection and the Internet personifies the location where these judgments are mainly sought, obtained, and evaluated. From this point of view, social networks are characterized by a significant effect. Due to this reason, Opinion Mining and Sentiment Analysis have found remarkable uses in various environments and one of the most interesting is embodied by public security and safety. The aim of this work is to study the perception of risk of aircraft passengers and users of airports of London (UK) and Rome (Italy) during COVID-19 pandemic. In particular, the airports of London Heathrow and Gatwick and the airports of Rome Fiumicino and Ciampino were studied, from March 23 to July 9, 2020, highlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, by means of the semantic analysis of the textual contents existing in Twitter.
Human Detection and Segmentation via Multi-view Consensus

Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data. However, existing approaches predominantly rely on restrictive assumptions on appearance and motion.For scenes with dynamic activities and camera motion, we propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training via coarse 3D localization in a voxel grid and fine-grained offset regression. In this manner, we learn a joint distribution of proposals over multiple views. At inference time, our method operates on single RGB images. We outperform state-of-the-art techniques both on images that visually depart from those of standard benchmarks and on those of the classical Human3.6M dataset.
Another Perspective on Automatic Construction of Integral Distinguishers for ARX Ciphers

This paper introduces a method to construct integral distinguishers for ARX ciphers. The basic idea of this method is to utilize the symmetry between the zero-correlation linear distinguishers and integral distinguishers. Combined with an automatic searching method on zero-correlation linear distinguishers of ARX ciphers, a subspace for the distinguishers is constructed. This subspace can finally be turned into an integral distinguisher based on the symmetry between these two distinguishers. Three ARX block ciphers, HIGHT, LEA and SPECK, are used to validate the effectiveness of this method. For LEA, four nine-round integral distinguishers are constructed, which is one more round than the previous best result derived with division property. For SPECK32, two more six-round integral distinguishers are constructed, whose number of active bits is reduced by one bit.
Chaotic Block Color Image Encryption Algorithm Using Cellular Automata and DNA Sequence

In this paper, a novel chaotic block color image encryption algorithm based on cellular automata and deoxyribonucleic acid (DNA) sequence is proposed. Firstly, the three components of the color image are converted to three DNA matrices by DNA encoding rules and combine into a new matrix. Secondly, divide the new matrix into several unequal blocks and elementary cellular automata (ECA) is introduced to effectively break the correlation of inter-intra components by dynamically swapping all blocks in the new matrix. Finally, double DNA operations are employed to hide the information of the image as much as possible by diffusing three permutated DNA matrices. In addition, a 512-bit plain image hash value and the external keys are introduced to generate the initial value for the chaotic system, which makes the algorithm is sensitive to the plain image. DNA encoding rules rely on plaintext information. Simulation results and security analyses demonstrate that our algorithm has the ability to resist various typical attacks and owns an acceptable running time.
Bridging Africa’s Income Inequality Gap: How Relevant Is China’s Outward FDI to Africa?

In line with the SDG 10 and Aspiration 1 of Africa’s Agenda 2063, this study examines whether: (i) the remarkable inflow of Chinese FDI to Africa matters for bridging the continent’s marked income inequality gap, (ii) Africa’s institutional fabric is effective in propelling Chinese FDI towards the equalisation of incomes in Africa, and (iii) there exist relevant threshold levels required for the various governance dynamics to cause Chinese FDI to equalise incomes in Africa. Our results, which are based on the dynamic GMM estimator for the period 1996 – 2020, reveal that though: (1) Chinese FDI contributes to equitable income distribution in Africa, the effect is weak, and (2) Africa’s institutional fabric matters for propelling Chinese FDI towards the equalisation of incomes across the continent, governance mechanisms for ensuring political stability, low corruption, and voice and accountability are keys. Finally, critical masses required for these three key governance dynamics to propel Chinese FDI to reduce income inequality are 0.8, 0.5 and 0.1, respectively. These critical masses are thresholds at which governance is a necessary but no longer a sufficient condition to complement Chinese FDI in order to mitigate income inequality. Hence, at the attendant thresholds, complementary policies are worthwhile. Policy recommendations are provided in the end.
IoT based automated Wheel Chair for Physically Challenged

The purpose of an automatic wheelchair (AWC) is to assist and support paraplegics. The proposed chair is particularly suited for people with a higher level of impairment, such as quadriplegics who are unable to move any of their body parts except their head due to their advanced age or infirmity. We created a microcontroller-based wheelchair that moves with the patient's head motion. Electronic and mechanical components make up the system. An accelerometer monitors the patient's head nodes in all four dimensions for head motion recognition. The paper goes over the Bluetooth technology on the wheelchair, as well as the accelerometer that powers the motors and controls the chair's motions inside the house. Our wheelchair is designed with the patient's health in mind as well as the device's maneuverability. The pulse rate and blood oxygen levels are monitored by sensors using an Internet of Things approach. The ultrasonic sensors aid with obstacle recognition, making it easier to move the chair in a certain direction. By observing the directions of head movement along the x and y axes, the implanted accelerometer aids the chair in determining which movement the patient wishes to make. The signal is then sent to the microcontroller. The wheelchair is controlled by a microprocessor-based on the direction of acceleration, and it moves in LEFT, RIGHT, FORWARD, and BACKWARD with the help of viper motors.
Emotional reactions to risk perception in the Herculaneum Archaeological Park

In safety and security management, it is important to evaluate the extent of perceived risk by tourists of heritage and cultural sites. Social media is very powerful in this regard as it facilitates reviews that influences the opinions of one by others. It is a key media of advertisement and 90% of tourists rely heavily on the reviews added to these sites. Research in opinion mining and sentiment analysis has gained importance in recent times with a need to enhance public safety and security. Such analysis aids security personnel to look out for unforeseen events that might lead to risks. In some cases, the perceived risks could be exaggerated. Therefore, there is a need to understand the reasons behind such anxiety and include mitigation in place. In this work, we consider the risk perception estimation for the Herculaneum Archaeological Park in Italy. We show how this can influence visitors emotionally by applying sentiment analysis on Twitter data.
FamilyFlower: an Artifical Flower to Foster Distant Family Connections

Modern, globalized lifestyles make physical separation of family members an inevitable part of life. Due to changes in routines and social roles, remaining connected to distant loved ones is challenging and adversely affects psychological well-being. To support relatedness, we created FamilyFlower, an artificial flower aimed at fostering a connection between remote households by bringing awareness of everyday activities and offering basic expressivity. In its environment, each device detects human presence, movement, sound, and touch. In the remote household, the paired prototype responds by respectively opening the flower bud, actuating the stem, altering the seeds' color, and dispensing a fragrance. In a 2-week study of deployment between 2 family members, we collected initial impressions of our prototype, and identified key aspects our system could support in future research. From our results, we see that FamilyFlower supports an awareness of ongoing activities in a distant household and built a feeling of connectedness.
Distance-based Probabilistic Data Augmentation for Synthetic Minority Oversampling

Class imbalance can adversely affect the performance of machine learning for prediction and classification. One approach to address the class imbalance problem is synthetic minority oversampling. Oversampling approaches can be broadly categorized as either being structural or statistical in nature. Structural approaches generally have the advantage of identifying and oversampling those minority data points that best facilitate class separation, while statistical approaches model the underlying distribution from which the minority samples can be drawn. In this article, we formulate a distance-based approach that generates samples by both modeling the underlying minority class distribution and by geometrically considering those borderline samples entangled in the majority class. We demonstrate the efficacy of our approach operating on the Class-Imbalance data set from UCI by comparing its mean accuracy, AUC and F 1 -score performance against both statistical and structural synthetic minority oversampling methods.
Performance Analysis for Channel-Weighted Federated Learning in OMA Wireless Networks

To alleviate the negative impact of noise on wireless federated learning (FL), we propose a channel-weighted aggregation scheme of FL (CWA-FL), in which the parameter server (PS) makes aggregation of the gradients according to the channel conditions of devices. In the proposed scheme, the gradients are transmitted to the PS in an uncoded way through an orthogonal multiple access (OMA) channel, which can avoid the synchronization issue among devices faced by over-the-air FL. The convergence analysis of CWA-FL is conducted and the theoretical results show that the scheme can converge with the rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O} (\frac{1}{T})$</tex-math></inline-formula> . Simulation results show that the proposed scheme performs better than the equal-weighted aggregation scheme of FL (EWA-FL) and is more robust to noise.