Reviewerly/exHarmony huggingface dataset:
Examining and mitigating gender bias in text emotion detection task

Gender bias is an important problem that affects models of natural language, and the propagation of such biases could be harmful. Much research focuses on gender biases in word embeddings, and there are also some works on gender biases in subsequent tasks. However, very limited prior work has been done on gender issues in emotion detection tasks. In this paper, we investigate the effect of gender in text emotion detection. Existing methods for gender biases require gender balanced and gender-swapping data, and might influence the performance of the target task due to removing more information related to sensitive attributes. We present different solutions to measuring and mitigating gender bias in emotion detection. To measure gender bias, we first prepare datasets annotated with emotional classes and gender information. Then, we compare the performance of emotion recognition models from gender balanced samples, and also analyze gender prediction results from emotion related data. Our experiment results show that there exists gender bias in emotion detection: the models trained on the female data often achieve better results than the male models, and the female models and the male models report the opposite trends on the recognition of some emotions. We also attempt to mitigate gender bias by developing various approaches including products of experts, introducing weights and variants of focal loss, as well as adversarial training. Compared to other debiasing methods, adversarial trainings represent tpr reduction approximately 0.02–0.03 while simultaneously less harming performance by below 1.0 points on our prepared datasets. Further, we show that efficient parameters can lead to further improvements.
Semantic Representations for NLP Using VerbNet and the Generative Lexicon

The need for deeper semantic processing of human language by our natural language processing systems is evidenced by their still-unreliable performance on inferencing tasks, even using deep learning techniques. These tasks require the detection of subtle interactions between participants in events, of sequencing of subevents that are often not explicitly mentioned, and of changes to various participants across an event. Human beings can perform this detection even when sparse lexical items are involved, suggesting that linguistic insights into these abilities could improve NLP performance. In this article, we describe new, hand-crafted semantic representations for the lexical resource VerbNet that draw heavily on the linguistic theories about subevent semantics in the Generative Lexicon (GL). VerbNet defines classes of verbs based on both their semantic and syntactic similarities, paying particular attention to shared diathesis alternations. For each class of verbs, VerbNet provides common semantic roles and typical syntactic patterns. For each syntactic pattern in a class, VerbNet defines a detailed semantic representation that traces the event participants from their initial states, through any changes and into their resulting states. The Generative Lexicon guided the structure of these representations. In GL, event structure has been integrated with dynamic semantic models in order to represent the attribute modified in the course of the event (the location of the moving entity, the extent of a created or destroyed entity, etc.) as a sequence of states related to time points or intervals. We applied that model to VerbNet semantic representations, using a class's semantic roles and a set of predicates defined across classes as components in each subevent. We will describe in detail the structure of these representations, the underlying theory that guides them, and the definition and use of the predicates. We will also evaluate the effectiveness of this resource for NLP by reviewing efforts to use the semantic representations in NLP tasks.
Obtaining Semi-Formal Models from Qualitative Data: From Interviews Into BPMN Models in User-Centered Design Processes

Gathering qualitative user data in a user-centered design process is one of the very early steps to create interactive systems. However, generating structured models from qualitative data towards descriptions that can be used for the implementation of interactive systems and prototypes raises various challenges, such as a strong influence of the modeler's knowledge and their interpretation of the gathered qualitative data. Introducing the modeler's bias may result in a system implementation which does not fully represent the information provided in the original qualitative data, generating an unwanted gap between what the user needs and what the system provides.To address this challenge, in this paper we present a structured and manual transformation method, which enables a modeler to create BPMN models from interview data by reducing the modeler's individual influence on the resulting BPMN model. We evaluate this approach in the context of the implementation of persuasive systems, which should support changing unwanted work-related habits. Therefore, we conducted unstructured interviews with office workers, thinking aloud interviews, in which we asked office workers to imagine a situation where they showed an unwanted work-related habit and to describe this habit together with an alternative behavior. In a quantitative experimental study, we then asked study participants to create BPMN models either with or without our new transformation method.Our analyses showed that when using our method, different participants created very similar BPMN models of the habits, even with little training. We conclude that the major contribution of our work is that the presented method can be applied to the creation of structured models from unstructured interview data. This method that makes use of rich interview data is suitable for the design and implementation of interactive systems.
A Biocentric Perspective on HCI Design Research Involving Plants

HCI researchers increasingly involve plants in their interaction design research, such as using plants as novel interfaces or sensors. While research involving other living entities, namely humans and animals is regulated, there exists no guidance for plants involved in HCI research. Motivated by biocentric philosophy, the present article makes a first attempt at addressing this gap. Commencing with a review of the development of research ethics, we show that as ethical concern has expanded from humans to animals, there is an argument for its expansion to plants. Through the lens of Respect for Nature, we then review the state-of-the-art in HCI design research involving plants. Our focus lies with ethics in the process of conducting research and its implications for the plants involved. We derive a set of actionable ethical design considerations for researchers involving plants in their interaction design work. Finally, our work aims to stimulate discussion on the involvement of plants in interaction design research.
A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach

Lymphomas, or cancers of the lymphatic system, account for around half of all blood cancers diagnosed each year. Lymphoma is a condition that is difficult to diagnose, and accurate diagnosis is critical for effective treatment. Manual microscopic analysis of blood cells requires the involvement of medical experts, whose precision is dependent on their abilities, and it takes time. This paper describes a content-based image retrieval system that uses deep learning-based feature extraction and a traditional learning method for feature reduction to retrieve similar images from a database to aid early/initial lymphoma diagnosis. The proposed algorithm employs a pre-trained network called ResNet-101 to extract image features required to distinguish four types of cells: lymphoma cells, blasts, lymphocytes, and other cells. The issue of class imbalance is resolved by over-sampling the training data followed by data augmentation. Deep learning features are extracted using the activations of the feature layer in the pre-trained net, then dimensionality reduction techniques are used to select discriminant features for the image retrieval system. Euclidean distance is used as the similarity measure to retrieve similar images from the database. The experimentation uses a microscopic blood image dataset with 1673 leukocytes of the categories blasts, lymphoma, lymphocytes, and other cells. The proposed algorithm achieves 98.74% precision in lymphoma cell classification and 99.22% precision @10 for lymphoma cell image retrieval. Experimental findings confirm our approach's practicability and effectiveness. Extended studies endorse the idea of using the prescribed system in actual medical applications, helping doctors diagnose lymphoma, dramatically reducing human resource requirements.
Cross subkey side channel analysis based on small samples

The majority of recently demonstrated Deep-Learning Side-Channel Analysis (DLSCA) use neural networks trained on a segment of traces containing operations only related to the target subkey. However, when the size of the training set is limited, as in this paper with only 5K power traces, the deep learning (DL) model cannot effectively learn the internal features of the data due to insufficient training data. In this paper, we propose a cross-subkey training approach that acts as a trace augmentation. We train deep-learning models not only on a segment of traces containing the SBox operation of the target subkey of AES-128 but also on segments for other 15 subkeys. Experimental results show that the accuracy of the subkey combination training model is [Formula: see text] higher than that of the individual subkey training model on traces captured in the microcontroller implementation of the STM32F3 with AES-128. And validation is performed on two additional publicly available datasets. At the same time, the number of traces that need to be captured when the model is trained is greatly reduced, demonstrating the effectiveness and practicality of the method.
Visual Analytics of Multivariate Intensive Care Time Series Data

Abstract We present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked‐view post hoc visual analytics application that reduces data complexity by combining projection‐based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non‐parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real‐world data: a post‐surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.
A Method for Prediction the Trajectory of Table Tennis in Multirotation State Based on Binocular Vision

The research on the space trajectory of high-speed moving and flying objects has very important research significance and application value in the fields of sports, military, aerospace, and industry. Table tennis has the characteristics of small size, fast flight speed, and complex motion model. It is very suitable as an experimental object for the study of flying object trajectory. This study takes table tennis as the research object to carry out research on the trajectory prediction of flying objects and builds a trajectory prediction system based on the trajectory prediction model, combining the constraints of the simple physical motion model and the deviation correction of the double LSTM neural network. Aiming at the problem of trajectory extraction of flying table tennis balls, a high-speed industrial camera was used to build a table tennis trajectory extraction system based on binocular vision. A multicamera information fusion method based on dynamic weights is proposed for the prediction of the trajectory of flying table tennis. In order to solve the problems that some model parameters are difficult to measure and the model is too complicated in the traditional physical motion model of table tennis trajectory, a method combining simple physics is proposed. This paper proposes a trajectory prediction model with motion model constraints and dual LSTM neural network bias correction. Experiments show that the proposed method can greatly improve the accuracy of the trajectory extraction and prediction system and can achieve a certain success rate of hitting.
Combining multiple granularity variability in a software product line approach for web engineering

Web engineering involves managing a high diversity of artifacts implemented in different languages and with different levels of granularity. Technological companies usually implement variable artifacts of Software Product Lines (SPLs) using annotations, being reluctant to adopt hybrid, often complex, approaches combining composition and annotations despite their benefits. This paper proposes a combined approach to support fine and coarse-grained variability for web artifacts. The proposal allows web developers to continue using annotations to handle fine-grained variability for those artifacts whose variability is very difficult to implement with a composition-based approach, but obtaining the advantages of the composition-based approach for the coarse-grained variable artifacts. A combined approach based on feature modeling that integrates annotations into a generic composition-based approach. We propose the definition of compositional and annotative variation points with custom-defined semantics, which is resolved by a scaffolding-based derivation engine. The approach is evaluated on a real-world web-based SPL by applying a set of variability metrics, as well as discussing its quality criteria in comparison with annotations, compositional, and combined existing approaches. Our approach effectively handles both fine and coarse-grained variability. The mapping between the feature model and the web artifacts promotes the traceability of the features and the uniformity of the variation points regardless of the granularity of the web artifacts. Using well-known techniques of SPLs from an architectural point of view, such as feature modeling, can improve the design and maintenance of variable web artifacts without the need of introducing complex approaches for implementing the underlying variability.
Single-User 2D Gaze Estimation in Retail Environment Using Deep Learning

Human gaze estimation is a widely used technique to observe human behavior. The rapid adaptation of deep learning techniques in gaze estimation has evolved human gaze estimation to many application domains. The retail industry is one domain with challenging unconstrained environmental conditions such as eye occlusion and personal calibration. This study presents a novel gaze estimation model for single-user 2D gaze estimation in a retail environment. Our novel architecture, inspired by the previous work in gaze following, models the scene and head feature and further utilizes a shifted grids technique to accurately predict a saliency map. Our results show that the model can effectively infer 2D gaze in a retail environment. We achieve state-of-the-art performance on Gaze On Objects (GOO) dataset. The obtained results have shown 25.2&#x00B0; angular error for gaze estimation. Furthermore, we provide a detailed analysis of the GOO dataset and comprehensively analyze the selected model feature extractor to support our results.
R-ACE Network for OLED Image Power Saving

Energy efficient display is essential to significantly reduce the power consumption of a device. OLED is an emissive display in which power consumption highly depends on pixel intensities of the emitted image. We propose two improvements for ACE network to produce low power image. First, we utilize L2 power-loss which results in faster training. Secondly, we add residual connection to the network and process the original luminance image. Evaluated in LIVE and BSD dataset with power reduction rate of 0.4, 0.6, and 0.8, our proposed method outperformed the original ACE network in most scenario.
Transfer learning and multi-phase training for accurate diacritization of Arabic poetry

Most Arabic poetry is undiacritized or partially diacritized (written without short vowels). For people of various ages and language mastery levels, diacritizing Arabic poetry would allow them to enjoy reading and chanting it easily and properly. Moreover, diacritizing a poetry verse is an essential step to analyze it for classification and evaluation. Unfortunately, the available automatic poetry diacritization solutions are inaccurate. Diacritizing Arabic poetry is a difficult task for people and machines alike because Arabic has numerous complex diacritization rules and Arabic poetry has additional special cases and rich and vibrant compositions. Deep machine learning could provide the desired diacritization solution provided that adequate training datasets are available. Unfortunately, the available datasets are insufficient and expensive to develop. In this paper, we propose solutions to improve the automatic diacritization of Arabic poetry using deep machine learning. We mitigate the difficulty of diacritizing Arabic poetry verses by employing transfer learning to leverage pattern features from a pretrained classification model. We also overcome the training dataset deficiency by training the composite diacritization model in multiple phases on carefully selected sub-datasets. Compared with best known previous results, the proposed solutions improve the diacritization error rate from 6.08% to 3.54% (42% improvement).
The amplitude of small eye movements can be accurately estimated with video-based eye trackers

Estimating the gaze direction with a digital video-based pupil and corneal reflection (P-CR) eye tracker is challenging partly since a video camera is limited in terms of spatial and temporal resolution, and because the captured eye images contain noise. Through computer simulation, we evaluated the localization accuracy of pupil-, and CR centers in the eye image for small eye rotations (≪ 1 deg). Results highlight how inaccuracies in center localization are related to 1) how many pixels the pupil and CR span in the eye camera image, 2) the method to compute the center of the pupil and CRs, and 3) the level of image noise. Our results provide a possible explanation to why the amplitude of small saccades may not be accurately estimated by many currently used video-based eye trackers. We conclude that eye movements with arbitrarily small amplitudes can be accurately estimated using the P-CR eye-tracking principle given that the level of image noise is low and the pupil and CR span enough pixels in the eye camera, or if localization of the CR is based on the intensity values in the eye image instead of a binary representation.
Random Graph-Based M-QAM Classification for MIMO Systems

Automatic modulation classification (AMC) has been identified to perform a key role to realize technologies such as cognitive radio, dynamic spectrum management, and interference identification that are arguably pivotal to practical SG communication networks. Random graphs (RGs) have been used to better understand graph behavior and to tackle combinatorial challenges in general. In this research article, a novel modulation classifier is presented to recognize M-Quadrature Amplitude Modulation (QAM) signals using random graph theory. The proposed method demonstrates improved recognition rates for multiple-input multiple-output (MIMO) and single-input single-output (SISO) systems. The proposed method has the advantage of not requiring channel/signal to noise ratio estimate or timing/frequency offset correction. Undirected RGs are constructed based on features, which are extracted by taking sparse Fourier transform (SFT) of the received signal. This method is based on the graph representation of the SFT of the 2nd, 4th, and 8th power of the received signal. The simulation results are also compared to existing state-of-the-art methodologies, revealing that the suggested methodology is superior.
Artificially Intelligent, Interactive, and Assistive Machines: A Definitional Framework for Intelligent Assistants

Intelligent assistants have proliferated across various settings spurring research and ideas. However, there is a lack of clarity on how they can be defined and identified. This definitional and terminological incoherence manifests in narrow and misinformed understanding of technology and thus, poses threats to theoretical and empirical endeavors in AI. To overcome these challenges, this paper presents a definitional framework that defines intelligent assistants and distinguishes them from other technologies. This framework is informed by tracing the historical evolution of this technology in the past 70 years which saw the rise of three constitutive features in machines: AI, interaction, and assistance. A review of technology’s definitions published since 2010 and associated terminologies was also conducted to unpack their conceptual limitations and support the framework. A new definition is then presented which contends that a technology can only be characterized as an intelligent assistant if it is AI-enabled, interactive, and assistive.
Optimal Multikey Homomorphic Encryption with Steganography Approach for Multimedia Security in Internet of Everything Environment

Recent developments of semiconductor and communication technologies have resulted in the interconnection of numerous devices in offering seamless communication and services, which is termed as Internet of Everything (IoE). It is a subset of Internet of Things (IoT) which finds helpful in several applications namely smart city, smart home, precise agriculture, healthcare, logistics, etc. Despite the benefits of IoE, it is limited to processing and storage abilities, resulting in the degradation of device safety, privacy, and efficiency. Security and privacy become major concerns in the transmission of multimedia data over the IoE network. Encryption and image steganography is considered effective solutions to accomplish secure data transmission in the IoE environment. For resolving the limitations of the existing works, this article proposes an optimal multikey homomorphic encryption with steganography approach for multimedia security (OMKHES-MS) technique in the IoE environment. Primarily, singular value decomposition (SVD) model is applied for the separation of cover images into RGB elements. Besides, optimum pixel selection process is carried out using coyote optimization algorithm (COA). At the same time, the encryption of secret images is performed using poor and rich optimization (PRO) with multikey homomorphic encryption (MKHE) technique. Finally, the cipher image is embedded into the chosen pixel values of the cover image to generate stego image. For assessing the better outcomes of the OMKHES-MS model, a wide range of experiments were carried out. The extensive comparative analysis reported the supremacy of the proposed model over the rennet approaches interms of different measures.