Reviewerly/exHarmony huggingface dataset:
Activity recognition and user preference learning for automated configuration of IoT environments

Internet of Things (IoT) environments encompass different types of devices and objects that offer a wide range of services. The dynamicity and uncertainty of those environments, including the mobility of users and devices, make it hard to foresee at design time available devices, objects, and services. For the users to benefit from such environments, they should be proposed services that are relevant to the specific context and can be provided by available things. Moreover, environments should be configured automatically based on users' preferences. To address these challenges, we propose an approach that leverages Artificial Intelligence techniques to recognize users' activities and provides relevant services to support users to perform their activities. Moreover, our approach learns users' preferences and configures their environments accordingly by dynamically forming, enacting, and adapting goal-driven IoT systems. In this paper, we present a conceptual model, a multi-tier architecture, and processes of our approach. Moreover, we report about how we validated the feasibility and evaluated the scalability of the approach through a prototype that we developed and used.
Microscopic scan-free surface profiling over extended axial ranges by point-spread-function engineering

The shape of a surface, i.e., its topography, influences many functional properties of a material; hence, characterization is critical in a wide variety of applications. Two notable challenges are profiling temporally changing structures, which requires high-speed acquisition, and capturing geometries with large axial steps. Here, we leverage point-spread-function engineering for scan-free, dynamic, microsurface profiling. The presented method is robust to axial steps and acquires full fields of view at camera-limited framerates. We present two approaches for implementation: fluorescence-based and label-free surface profiling, demonstrating the applicability to a variety of sample geometries and surface types.
Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.
Adapting to online teaching in software engineering courses

The COVID-19 worldwide pandemic caused sudden and unexpected changes in how we teach software engineering and other university courses. This paper presents an empirical study that aims to improve our understanding on how the assessment of student learning changed, in response to the transition from in-class to online courses. A questionnaire was distributed to instructors across the globe. The results indicate that the evaluation methodologies for most reported learning objectives have changed. Not surprising, in-class oral presentations and in-class exams are no longer used by the instructors for evaluations. We observed a trend of having fewer exams and more project-related evaluations after the transition. Not all instructors changed the way they evaluated student learning after the transition, however the majority reported their effort in student learning assessment increased after the transition, whether they made changes in methodologies or not.
Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift

Target shift, the different label distributions of source and target domains, is an important problem for practical use of unsupervised domain adaptation (UDA); as we do not know labels in target domain datasets, we cannot ensure an identical label distribution between the two domains. Despite this inaccessibility, modern UDA methods commonly try to match the shape of the feature distributions over the domains while projecting the features to labels by a common classifier. This implicitly assumes the identical label distribution. To overcome this problem, we propose a method that generates a pseudo pair by domain conversion where the label is preserved identically even trained with target-shifted datasets. A pair-wise metric learning enables to align feature over the domains without matching the shape of distributions. We conducted two experiments: one is a regression of pose-estimation, where label distribution is continuous and the target shift problem can seriously degrade the quality of UDA. The other is digit classification task where we can systematically control the distribution difference. The code and dataset are available at https://github.com/iiyama-lab/PS-VAEs.
DeepStyle: User Style Embedding for Authorship Attribution of Short Texts

Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have shown that deep learning methods could achieve significant accuracy improvement for the AA task. Nevertheless, most of these proposed methods represent user posts using a single type of features (e.g., word bi-grams) and adopt a text classification approach to address the task. Furthermore, these methods offer very limited explainability of the AA results. In this paper, we address these limitations by proposing DeepStyle, a novel embedding-based framework that learns the representations of users' salient writing styles. We conduct extensive experiments on two real-world datasets from Twitter and Weibo. Our experiment results show that DeepStyle outperforms the state-of-the-art baselines on the AA task.
Retrofitting post-quantum cryptography in internet protocols

Quantum computing is threatening current cryptography, especially the asymmetric algorithms used in many Internet protocols. More secure algorithms, colloquially referred to as Post-Quantum Cryptography (PQC), are under active development. These new algorithms differ significantly from current ones. They can have larger signatures or keys, and often require more computational power. This means we cannot just replace existing algorithms by PQC alternatives, but need to evaluate if they meet the requirements of the Internet protocols that rely on them. In this paper we provide a case study, analyzing the impact of PQC on the Domain Name System (DNS) and its Security Extensions (DNSSEC). In its main role, DNS translates human-readable domain names to IP addresses and DNSSEC guarantees message integrity and authenticity. DNSSEC is particularly challenging to transition to PQC, since DNSSEC and its underlying transport protocols require small signatures and keys and efficient validation. We evaluate current candidate PQC signature algorithms in the third round of the NIST competition on their suitability for use in DNSSEC. We show that three algorithms, partially, meet DNSSEC's requirements but also show where and how we would still need to adapt DNSSEC. Thus, our research lays the foundation for making DNSSEC, and protocols with similar constraints ready for PQC.
A Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation

Many Metaheuristics solve optimization problems in the continuous domain, so it is necessary to apply binarization schemes to solve binary problems, this selection that is not trivial since it impacts the heart of the search strategy: its ability to explore. This paper proposes a Hyperheuristic Binarization Framework based on a Machine Learning technique of Reinforcement Learning to select the appropriate binarization strategy, which is applied in a Low Level Metaheuristic. The proposed implementation is composed of a High Level Metaheuristic, Ant Colony Optimization, using Q-Learning replacing the pheromone trace component. In the Low Level Metaheuristic, we use a Grey Wolf Optimizer to solve the binary problem with binarization scheme fixed by ants. This framework allowing a better balance between exploration and exploitation, and can be applied selecting others low level components.
A Tversky Loss-Based Convolutional Neural Network for Liver Vessels Segmentation

The volumetric estimation of organs is a crucial issue both for the diagnosis or assessment of pathologies and for surgical planning. Three-dimensional imaging techniques, e.g. Computed Tomography (CT), are widely used for this task, allowing to perform 3D analysis based on the segmentation of each bi-dimensional slice. In this paper, we considered a fully automatic setup based on Convolutional Neural Networks (CNNs) for the semantic segmentation of human liver parenchyma and vessels in CT scans. Vessels segmentation is also crucial for surgical planning as it allows separating the liver into anatomical segments, each with its own vascularization.
Using Image Registration and Machine Learning to Develop a Workstation Tool for Rapid Analysis of Glomeruli in Medical Renal Biopsies

Prescreening of biopsies has the potential to improve pathologists' workflow. Tools that identify features and display results in a visually thoughtful manner can enhance efficiency, accuracy, and reproducibility. Machine learning for detection of glomeruli ensures comprehensive assessment and registration of four different stains allows for simultaneous navigation and viewing.Medical renal core biopsies (4 stains each) were digitized using a Leica SCN400 at ×40 and loaded into the Corista Quantum research platform. Glomeruli were manually annotated by pathologists. The tissue on the 4 stains was registered using a combination of keypoint- and intensity-based algorithms, and a 4-panel simultaneous viewing display was created. Using a training cohort, machine learning convolutional neural net (CNN) models were created to identify glomeruli in all stains, and merged into composite fields of views (FOVs). The sensitivity and specificity of glomerulus detection, and FOV area for each detection were calculated.Forty-one biopsies were used for training (28) and same-batch evaluation (6). Seven additional biopsies from a temporally different batch were also evaluated. A variant of AlexNet CNN, used for object recognition, showed the best result for the detection of glomeruli with same-batch and different-batch evaluation: Same-batch sensitivity 92%, "modified" specificity 89%, average FOV size represented 0.8% of the total slide area; different-batch sensitivity 90%, "modified" specificity 98% and average FOV size 1.6% of the total slide area.Glomerulus detection in the best CNN model shows that machine learning algorithms may be accurate for this task. The added benefit of biopsy registration with simultaneous display and navigation allows reviewers to move from one machine-generated FOV to the next in all 4 stains. Together these features could increase both efficiency and accuracy in the review process.
Application of convolutional neural network to traditional data

Convolutional neural networks (ConvNets) have been applied to various types of data, including image, text, and speech, but not to traditional data. In this study, traditional data are defined as data whose features have no spatial or temporal dependencies but might have statistical correlations. We construct a feature grid-based ConvNet (FGCN) model for classification tasks on traditional data. The FGCN model is composed of two functional parts: The first is used to convert traditional data in the form of a 1-D feature vector into a 1-D, 2-D, or higher-dimensional feature grid; and the second is a ConvNet classifier for the converted data. The experimental results show that the FGCN model performs well; therefore, it is worth considering this model for classification tasks on traditional data.
Intelligent Transportation Application and Analysis for Multi-Sensor Information Fusion of Internet of Things

During the driving of the smart car, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot fully perceive the traffic environment around the smart car. Therefore, it is necessary to study the information fusion scheme of heterogeneous sensors, use the advantages of heterogeneous sensors to make up for the shortcomings of a single sensor, to realize the function of cooperation and mutual compensation among multiple heterogeneous sensors. Therefore, this paper proposes a data fusion method based on discrete factor multi-sensor target recognition. The output data from multiple sensors acquired over too many periods and multiple regions give the discrete factor of the sensor corresponding to the target characteristic. The current weight of multi-sensor target recognition is given according to the discrete factor, and the relative consistency and the relativeness of multi-sensor target recognition is established. Weighted consistency and other functions; combined with the current weights of multi-sensor target recognition and related consistency functions, a data fusion support calculation model for multi-sensor target recognition is constructed. The test results show that the scheme is more reliable and has a certain anti-interference ability, which can make up for the shortcomings of a single sensor and improve the target recognition rate.
Phrase embedding learning from internal and external information based on autoencoder

Phrase embedding can improve the performance of multiple NLP tasks. Most of the previous phrase-embedding methods that only use the external or internal semantic information of phrases to learn phrase embedding are challenging to solve the problem of data sparseness and have poor semantic presentation ability. To solve the above issues, in this paper, we propose an autoencoder-based method to combine pre-trained phrase embeddings and phrase component word embeddings into new phrase embeddings through complex non-linear transformations. This method uses both internal and external semantic information of phrases to generate new phrases with better semantic expression capabilities. This method can also generate well-represented phrase embeddings when only pre-trained component word embeddings are used as input to solve the problem of data sparseness effectively. We have designed two models for this method. The first one uses an FCNN(Fully Connected Neural Network) as the encoder and decoder, which we call AE-F. The second one uses the attention mechanism shared by the parameters of encoder and decoder to proportionally allocate the outputs of an LSTM and an FCNN, which we call it AE-ALF. We evaluated them in terms of phrase similarity and phrase classification and used two English datasets and two Chinese datasets. Experimental results show that AE-F and AE-ALF methods using pre-trained phrase embeddings and component word embeddings exceed 17 baseline methods, and AE-F and AE-ALF perform similarly. With only pre-trained component word embeddings, AE-F and AE-ALF also exceed most baseline methods, and AE-ALF performs better than AE-F.
Primitive values of rational functions at primitive elements of a finite field

Given a prime power q and an integer n≥2, we establish a sufficient condition for the existence of a primitive pair (α,f(α)) where α∈Fq and f(x)∈Fq(x) is a rational function of degree sum n. (Here f=f1/f2, where f1,f2 are coprime polynomials of degree n1,n2, respectively, and the sum of their degrees n1+n2=n.) For any n, such a pair is guaranteed to exist for sufficiently large q. Indeed, when n=2, such a pair definitely does not exist only for 28 values of q and possibly (but unlikely) only for at most 3911 other values of q.
Examining the modelling capabilities of defeasible argumentation and non-monotonic fuzzy reasoning

Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload and its assessment. Different argument-based and non-monotonic fuzzy reasoning models have been designed considering knowledge-bases of incremental complexity containing uncertain and conflicting information provided by a human reasoner. Findings showed how their inferences have a moderate convergent and face validity when compared respectively to those of an existing baseline instrument for mental workload assessment, and to a perception of mental workload self-reported by human participants. This confirmed how these models also reasonably represent the construct under consideration. Furthermore, argument-based models had on average a lower mean squared error against the self-reported perception of mental workload when compared to fuzzy-reasoning models and the baseline instrument. The contribution of this research is to provide scholars, interested in formalisms on knowledge-representation and non-monotonic reasoning, with a novel approach for empirically comparing their inferential capacity.
Harmony Search-Based Approach for Multi-Objective Software Architecture Reconstruction

The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult to understand and maintain. To improve the architecture of a software system, multiple design goals or objectives (often conflicting) need to be optimized simultaneously. To address such types of multi-objective optimization problems a variety of metaheuristic-oriented computational intelligence algorithms have been proposed. In existing approaches, harmony search (HS) algorithm has been demonstrated as an effective approach for numerous types of complex optimization problems. Despite the successful application of the HS algorithm on different non-software engineering optimization problems, it gained little attention in the direction of architecture reconstruction problem. In this study, we customize the original HS algorithm and propose a multi-objective harmony search algorithm for software architecture reconstruction (MoHS-SAR). To demonstrate the effectiveness of the MoHS-SAR, it has been tested on seven object-oriented software projects and compared with the existing related multi-objective evolutionary algorithms in terms of different software architecture quality metrics and metaheuristic performance criteria. The experimental results show that the MoHS-SAR performs better compared to the other related multi-objective evolutionary algorithms.
Exploring Understandable Algorithms to Suggest Fitness Tracker Goals that Foster Commitment

While fitness trackers are gaining popularity, they struggle to offer long-term health benefits, largely due to their inability to offer engaging goals. Understanding how trackers can suggest and update fitness goals can lead to building improved systems that support wellbeing. We investigate how to suggest fitness tracker goals to users and ways to help them commit to those goals. We compared algorithms for step goal setting in a pre-study. Next, we conducted two surveys (a vignette study and a survey using the users' Fitbit data) that compared the users' attitudes to suggested goals, with and without disclosing the algorithm to them. We found that explaining how a step goal was computed increased goal commitment and, in one study, contributed to building trust in the goal. Our work shows that explaining how a tracker works can help build engaging fitness tracking experiences. We contribute insights on designing transparent personal informatics systems.
A Framework for Adapting DNN Speaker Embedding Across Languages

Language mismatch remains a major hindrance to the extensive deployment of speaker verification (SV) systems. Current language adaptation methods in SV mainly rely on linear projection in embedding space; i.e., adaptation is carried out after the speaker embeddings have been created, which underutilizes the powerful representation of deep neural networks. This article proposes a maximum mean discrepancy (MMD) based framework for adapting deep neural network (DNN) speaker embedding across languages, featuring multi-level domain loss, separate batch normalization, and consistency regularization. We refer to the framework as MSC. We show that (1) minimizing domain discrepancy at both frame- and utterance-levels performs significantly better than at utterance-level alone; (2) separating the source-domain data from the target-domain in batch normalization improves adaptation performance; and (3) data augmentation can be utilized in the unlabelled target-domain through consistency regularization. By combining these findings, we achieve an EER of 8.69% and 7.95% in NIST SRE 2016 and 2018, respectively, which are significantly better than the previously proposed DNN adaptation methods. Our framework also works well with backend adaptation. By combining the proposed framework with backend adaptation, we achieve an 11.8% improvement over the backend adaptation in SRE18. When applying our framework to a 121-layer Densenet, we achieved an EER of 7.81% and 7.02% in NIST SRE 2016 and 2018, respectively.
A Novel Posit-based Fast Approximation of ELU Activation Function for Deep Neural Networks

Nowadays, real-time applications are exploiting DNNs more and more for computer vision and image recognition tasks. Such kind of applications are posing strict constraints in terms of both fast and efficient information representation and processing. New formats for representing real numbers have been proposed and among them the Posit format appears to be very promising, providing means to implement fast approximated version of widely used activation functions in DNNs. Moreover, information processing performance are continuously improved thanks to advanced vectorized SIMD (single-instruction multiple-data) processor architectures and instructions like ARM SVE (Scalable Vector Extension). This paper explores both approaches (Posit-based implementation of activation functions and vectorized SIMD processor architectures) to obtain faster DNNs. The two proposed techniques are able to speed up both DNN training and inference steps.
A Python framework for the automated analysis of feature models

Feature modeling is the "de facto" standard to describe the common and variant parts of software product lines. Different tools, approaches, and operations for the automated analysis of feature models (AAFM) have been proposed in the last 20 years. The increasing popularity of languages such as Python made the usage of AAFM techniques require lots of integration efforts with exiting Java-based tools. In this paper, we present a design for a Python-based framework to analyze feature models. This framework implements the most common operations while enabling support for multiple solvers and backends.