Human Intention Research Articles

Human Resources Management Practice and Behavioural Intention on Internet Free Banking in Nigerian Deposit Money Banks

The study looked at the impact of human resource management techniques on clients’ willingness to sign up for free internet banking at Nigerian Deposit Money Bank. The study was conducted using a descriptive survey research approach. The study population is made up of employees from various Deposit Money locations. The sample size was 167 people, chosen using a census sampling technique. The study’s primary data was acquired through a standardised questionnaire. The collected data was evaluated using a regression model. The findings revealed that recruitment has a positive impact on client behavioural intention; that performance appraisal has a positive impact on client behavioural intention; and that employee participation has a positive impact on client behavioural intention. As a result, it was concluded that human resources management practices have a positive impact on client behavioural intention, based on which the recommendation was made.

Integrating deep neural network with logic rules for credit scoring

Credit scoring is an important topic in financial activities and bankruptcy prediction that has been extensively explored using deep neural network (DNN) methods. DNN-based credit scoring models rely heavily on a large amount of labeled data. The accuracy of DNN-based credit assessment models relies heavily on large amounts of labeled data. However, purely data-driven learning makes it difficult to encode human intent to guide the model to capture the desired patterns and leads to low transparency of the model. Therefore, the Probabilistic Soft Logic Posterior Regularization (PSLPR) framework is proposed for integrating prior knowledge of logic rule with neural network. First, the PSLPR framework calculates the rule satisfaction distance for each instance using a probabilistic soft logic formula. Second, the logic rules are integrated into the posterior distribution of the DNN output to form a logic output. Finally, a novel discrepancy loss which measures the difference between the real label and the logic output is used to incorporate logic rules into the parameters of the neural network. Extensive experiments were conducted on two datasets, the Australian credit dataset and the credit card customer default dataset. To evaluate the obtained systems, several performance metrics were used, including PCC, Recall, F1 and AUC. The results show that compared to the standard DNN model, the four evaluation metrics are increased by 7.14%, 14.29%, 8.15%, and 5.43% respectively on the Australian credit dataset.

Myoelectric human computer interaction using CNN-LSTM neural network for dynamic hand gesture recognition

Human-computer interaction(HCI) has broad range of applications. One particular application domain is rehabilitation devices. Several bioelectric signals can potentially be used in HCI systems in general and rehabilitation devices in particular. Surface ElectroMyoGraphic(sEMG) signal is one of the more important bioelectric signals in this context. The sEMG signal is formed by muscle activation although the details are rather complex. Applications of sEMG are referred is commonly referred to as myoelectric control since the dominant use of this signal is to activate a device even if (as the term control may imply) feedback is not always used in the process. With the development of deep neural networks, various deep learning architectures are used for sEMG-based gesture recognition with many researchers having reported good performance. Nevertheless, challenges remain in accurately recognizing sEMG patterns generated by gestures produced by hand or the upper arm. For instance one of the difficulties in hand gesture recognition is the influence of limb positions. Several papers have shown that the accuracy of gesture classification decreases when the limb position changes even if the gesture remains the same. Prior work by our team has shown that dynamic gesture recognition is in principle more reliable in detecting human intent, which is often the underlying idea of gesture recognition. In this paper, a Convolutional Neural Network (CNN) with Long Short-Term Memory or LSTM (CNN-LSTM) is proposed to classify five common dynamic gestures. Each dynamic gesture would be performed in five different limb positions as well. The trained neural network model is then used to enable a human subject to control a 6 DoF (Degree of Freedom) robotic arm with 1 DoF gripper. The results show a high level of accurate performance achieved with the proposed approach. In particular, the overall accuracy of the dynamic gesture recognition is 84.2%. The accuracies vary across subjects but remain at approximately 90%for some subjects.

The Nature and Limits of Theology: A Response to Rowan Williams

AbstractTheology is both a human endeavor and something of Divine origin, insofar as it is a human attempt to make sense of certain Divinely revealed propositions. How does one reconcile these two elements? One attempt to do so was on the part of the Anglican theologian Rowan Williams. In sections I and II of chapter 1 of his work ‘On Christian Theology’, Williams speaks of the nature of authentic theological discourse, that is, theological discourse that has integrity. In his work on theological integrity, Williams explores how human intentions or hidden agendas can potentially warp our attempt to make sense of the truths contained within Divine Revelation. He then goes on to speak of how to avoid such pitfalls. In this article, I will respond to the epistemological implications of William's thoughts on this topic. I will use various ideas from Catholic theology, including the development of doctrines and the notion that faith has both a subjective and objective element, to explore both the strengths and weaknesses of Williams's thought.

Joint Kinematics, Kinetics and Muscle Synergy Patterns During Transitions Between Locomotion Modes.

There is an increasing demand for accurately predicting human movement intentions. To be effective, predictions must be performed as early as possible in the preceding step, though precisely how early has been studied relatively little; how and when a person's movement patterns in a transition step deviate from those in the preceding step must be clearly defined. In this study, we collected motion kinematics, kinetics and electromyography data from 9 able-bodied participants during 7 locomotion modes. Twelve types of steps between the 7 locomotion modes were studied, including 5 continuous steps (taking another step in the same locomotion mode) and 7 transitions steps (taking a step from one locomotion mode into another). For each joint degree of freedom, joint angles, angular velocities, moments, and moment rates were compared between continuous steps and transition steps, and the relative timing during the transition step at which these parameters diverged from those of a continuous step, which we refer to as transition starting times, were identified using multiple analyses of variance. Muscle synergies were also extracted for each step, and we studied in which locomotion modes these synergies were common (task-shared) and in which modes they were specific (task-specific). The transition starting times varied among different transitions and joint degrees of freedom. Most transitions started in the swing phase of the transition step. These findings can be applied to determine the critical timing at which a powered assistive device must adapt its control to enable safe and comfortable support to a user.

The sacred unbound

Dissonances between human expectations and actual experiences of sacred presence and actions—as materialized in unexplained deaths, diseases, deadly possessions, and repudiated rituals—are sites where insubstantial sacralities are not only made real but also agentic. Refusing human attempts to relate to it through choreographed rituals and voluntary offerings, this sacred asserts its own agenda. To make inscrutable sacralities ethnographically accessible, I propose the twin pivots of (1) insatiability and (2) excess—attributes of presence, appetite, and attitude—which embody a repudiation of anthropocentric signification. Decentering human intentions and actions allows for the excavation of an uncanniness intrinsic to the sacred. This forces a confrontation with the limits of anthropological epistemologies, language, and authority. Privileging instances when the sacred eludes epistemological capture to assert its own irrepressible and enigmatic vitality, this article strives to—not explain away radical ontological differences but—make room for an uncanny metaphysics amid ethnographic theorizing.

Magic, metaphysics, and methods

Magic, metaphysics, and methods

Research on Lower Limb Step Speed Recognition Method Based on Electromyography.

Wearable exoskeletons play an important role in people's lives, such as helping stroke and amputation patients to carry out rehabilitation training and so on. How to make the exoskeleton accurately judge the human action intention is the basic requirement to ensure that it can complete the corresponding task. Traditional exoskeleton control signals include pressure values, joint angles and acceleration values, which can only reflect the current motion information of the human lower limbs and cannot be used to predict motion. The electromyography (EMG) signal always occurs before a certain movement; it can be used to predict the target's gait speed and movement as the input signal. In this study, the generalization ability of a BP neural network and the timing property of a hidden Markov chain are used to properly fuse the two, and are finally used in the research of this paper. Experiments show that, using the same training samples, the recognition accuracy of the three-layer BP neural network is only 91%, while the recognition accuracy of the fusion discriminant model proposed in this paper can reach 95.1%. The results show that the fusion of BP neural network and hidden Markov chain has a strong solving ability for the task of wearable exoskeleton recognition of target step speed.

Multi-Scopic Cognitive Memory System for Continuous Gesture Learning.

With the advancement of artificial intelligence technologies in recent years, research on intelligent robots has progressed. Robots are required to understand human intentions and communicate more smoothly with humans. Since gestures can have a variety of meanings, gesture recognition is one of the essential issues in communication between robots and humans. In addition, robots need to learn new gestures as humans grow. Moreover, individual gestures vary. Because catastrophic forgetting occurs in training new data in traditional gesture recognition approaches, it is necessary to preserve the prepared data and combine it with further data to train the model from scratch. We propose a Multi-scopic Cognitive Memory System (MCMS) that mimics the lifelong learning process of humans and can continuously learn new gestures without forgetting previously learned gestures. The proposed system comprises a two-layer structure consisting of an episode memory layer and a semantic memory layer, with a topological map as its backbone. The system is designed with reference to conventional continuous learning systems in three ways: (i) using a dynamic architecture without setting the network size, (ii) adding regularization terms to constrain learning, and (iii) generating data from the network itself and performing relearning. The episode memory layer clusters the data and learns their spatiotemporal representation. The semantic memory layer generates a topological map based on task-related inputs and stores them as longer-term episode representations in the robot's memory. In addition, to alleviate catastrophic forgetting, the memory replay function can reinforce memories autonomously. The proposed system could mitigate catastrophic forgetting and perform continuous learning by using both machine learning benchmark datasets and real-world data compared to conventional methods.

Working conditions and Human Capital Flight Intentions among Medical Doctors in Federal Tertiary Healthcare Institutions in South-South Nigeria

This study was carried out to assess the influence of working conditions on human capital flight intention of medical doctors in federal tertiary healthcare institutions, south-south, Nigeria. This was premised on the fact that medical doctors are leaving the country in droves while others may be harboring the intention of leaving. Push-Pull theory was used in supporting this study. Survey research design was adopted for this study and the study population was 2,337 medical doctors in the employ of the various tertiary healthcare institutions within the study area from which a sample of 342 was selected. Primary data, which were collected through a structured questionnaire, was used for the study. Data obtained were analyzed using descriptive statistics and inferential techniques. Results of data analysis revealed that physical work environment explains 7.3% of variance to human capital flight; work burden contributes 3.9% while supervisor support explained 3.7%. Conclusively, the heavy loss of medical doctors including the ones that intend to migrate, poses threat of collapse in the healthcare sector and present major risks to the lives of Nigerians. This calls for intensive consultation and consensus building between the developing and developed countries.

A method based on interpretable machine learning for recognizing the intensity of human engagement intention

To interact with humans more precisely and naturally, social robots need to “perceive” human engagement intention, especially need to recognize the main interaction person in multi-person interaction scenarios. By analyzing the intensity of human engagement intention (IHEI), social robots can distinguish the intention of different persons. Most existing research in this field mainly focus on analyzing whether a person has the intention to interact with the robot while lack of analysis of IHEI. In this regard, this paper proposes an approach for recognizing the engagement intention intensity. Four categories of visual features, including line of sight, head pose, distance and expression of human, are captured, and a CatBoost-based machine learning model is applied to train an optimal classifier for predicting the IHEI on the dataset. The experimental results show that this classifier can effectively predict the IHEI that can be applied into real human–robot interaction scenarios. Moreover, the proposed model is an interpretable machine learning model, where interpretability analysis on the trained classifier has been done to explore the deep associations between input features and engagement intention, thereby providing robust and effective robot social decision-making.

Factors related to human papillomavirus vaccine uptake and intentions among adults aged 18-26 and 27-45 years in the United States: A cross-sectional study.

The current study examined self-reported human papillomavirus (HPV) vaccination uptake and intentions, and associations with psychosocial constructs among United States adults aged 18 to 26 and 27 to 45 years. Data were collected via an online survey from participants recruited from a research panel. Outcomes were HPV vaccination uptake and intentions. Multivariable binary and ordered logistic regression models were used to examine associations between HPV vaccination outcomes and psychosocial constructs, sociodemographics, and previous health behaviors. HPV vaccination uptake in both age cohorts (N= 2722) was associated with multiple variables, including but not limited to: provider recommendation (adjusted odds ratio [aOR], 11.63 [95% CI, 7.70-17.56] and aOR, 14.26 [95% CI, 9.52-21.38], for those aged 18 to 26 and 27 to 45 years, respectively) and positive HPV vaccine attitudes (aOR, 2.40 [95% CI, 1.70-3.40] and aOR, 1.46 [95% CI,1.06-2.02]). Among those who did not report or were unsure of prior HPV vaccination (N=1894), only 4.6% and 8.1% (aged 18-26 and 27-45 years, respectively) reported being very likely to receive the HPV vaccine in the next year. Increased intentions were associated with more positive vaccine attitudes (aOR, 2.45 [95% CI, 1.91-3.15] and aOR, 2.19 [95% CI, 1.72-2.78]) and provider recommendation (yes vs no; aOR, 1.97 [95% CI, 1.38-2.83] and aOR, 1.82 [95% CI, 1.31-2.52]; don't know/can't remember vs no; aOR, 1.38 [95% CI, 1.03-1.84] and aOR, 1.60 [95% CI, 1.17-2.18]). Sociodemographics and health behaviors associated with increased intentions differed for each age cohort. Individual and interpersonal factors were associated with HPV vaccination uptake and intentions. Findings reveal the need for targeted interventions to improve HPV vaccination rates among these age groups.

Data augmentation by separating identity and emotion representations for emotional gait recognition

AbstractHuman-centered intelligent human–robot interaction can transcend the traditional keyboard and mouse and have the capacity to understand human communicative intentions by actively mining implicit human clues (e.g., identity information and emotional information) to meet individuals’ needs. Gait is a unique biometric feature that can provide reliable information to recognize emotions even when viewed from a distance. However, the insufficient amount and diversity of training data annotated with emotions severely hinder the application of gait emotion recognition. In this paper, we propose an adversarial learning framework for emotional gait dataset augmentation, with which a two-stage model can be trained to generate a number of synthetic emotional samples by separating identity and emotion representations from gait trajectories. To our knowledge, this is the first work to realize the mutual transformation between natural gait and emotional gait. Experimental results reveal that the synthetic gait samples generated by the proposed networks are rich in emotional information. As a result, the emotion classifier trained on the augmented dataset is competitive with state-of-the-art gait emotion recognition works.

Dynamic Attention-Based CVAE-GAN for Pedestrian Trajectory Prediction

Predicting pedestrian trajectories is crucial for human-interactive systems. This task is compounded by the inherently multimodal nature of human motions and complex external and internal factors such as social interactions and intentions. In this letter, we propose a dynamic attention-based CVAE-GAN method that simultaneously models time-varying social interactions and human intentions while generating multimodal trajectory predictions. Herein, CVAE is used to account for the multimodality of trajectories explicitly, and GAN is used for adversarial training. A spatio-temporal graph with dynamic attention is leveraged to encode pedestrian interactions, which are further fed into a recurrent neural network to capture the temporal dependencies of evolving patterns. Moreover, we aggregate all potential sub-goals from current position to the destination with another dynamic attention module to shape the predicted trajectories. Experiments on widely used ETH and UCY benchmarks will verify the effectiveness of the proposed method. In particular, the proposed method could outperform the state-of-the-art by approximately 10% in terms of prediction errors and 40% considering collision avoidance.

Online Classification of Transient EMG Patterns for the Control of the Wrist and Hand in a Transradial Prosthesis

Decoding human motor intentions by processing electrophysiological signals is a crucial, yet unsolved, challenge for the development of effective upper limb prostheses. Pattern recognition of continuous myoelectric (EMG) signals represents the state-of-art for multi-DoF prosthesis control. However, this approach relies on the unreliable assumption that repeatable muscular contractions produce repeatable patterns of steady-state EMGs. Here, we propose an approach for decoding wrist and hand movements by processing the signals associated with the onset of contraction (transient EMG). Specifically, we extend the concept of a transient EMG controller for the control of both wrist and hand, and tested it online. We assessed it with one transradial amputee and 15 non-amputees via the Target Achievement Control test. Non-amputees successfully completed 95% of the trials with a median completion time of 17 seconds, showing a significant learning trend ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001). The transradial amputee completed about the 80% of the trials with a median completion time of 26 seconds. Although the performance proved comparable with earlier studies, the long completion times suggest that the current controller is not yet clinically viable. However, taken collectively, our outcomes reinforce earlier hypothesis that the transient EMG could represent a viable alternative to steady-state pattern recognition approaches.

Toward Error-Tolerant Robot Navigation: Sequential Inducement Based on Intent Conveyance from Robot to Human and Its Achievement

Human symbiotic mobile robots are required to smoothly reach destinations while avoiding humans. Human-intent information, e.g., a moving direction, must be carefully estimated since its lack leads to the hesitation and repetitious avoidance. Conventional studies address human intent estimation and conveyance but do not consider cases of failing communication as a systematic framework, even though the intent is essentially difficult for humans to estimate due to its interiority. In response to this problem, we propose a framework of error-tolerant navigation (ETN) with a process to actively estimate the human intent by iterative interaction from the robot. As a preliminary study, we focus on ‘the intent conveyance from robot to human’ and ‘its achievement’ as core information. The ETN estimates interference possibility to determine the need for inducement, human awareness (HA) to select an inducement method, and inducement achievement (IA) to judge the need for action again. If the ETN estimates the interference, the robot provides inducements according to HA, e.g., route indication when HA is high or voice/physical interaction when HA is low. Each inducement corresponds to an expected behavior change in the human. IA is calculated from the difference between the expected and actual changes. If the robot observes no change within a specified time after the inducement, it executes inducements with a stronger intent conveyance. When IA is none after the strongest action, it selects another route. This error-collection loop in the ETN could prevent a fatal mistake by recognizing a small mistake and recovering it. The static and dynamic experimental results indicated that the ETN could achieve smoother human movement and reduce psychological burden by correcting the robot behaviors, compared with a conventional navigation system, which can contribute to constructing a practical ETN framework.

Stable Heteroclinic Channel Networks for Physical Human-Humanoid Robot Collaboration.

Human-robot collaboration is one of the most challenging fields in robotics, as robots must understand human intentions and suitably cooperate with them in the given circumstances. But although this is one of the most investigated research areas in robotics, it is still in its infancy. In this paper, human-robot collaboration is addressed by applying a phase state system, guided by stable heteroclinic channel networks, to a humanoid robot. The base mathematical model is first defined and illustrated on a simple three-state system. Further on, an eight-state system is applied to a humanoid robot to guide it and make it perform different movements according to the forces exerted on its grippers. The movements presented in this paper are squatting, standing up, and walking forwards and backward, while the motion velocity depends on the magnitude of the applied forces. The method presented in this paper proves to be a suitable way of controlling robots by means of physical human-robot interaction. As the phase state system and the robot movements can both be further extended to make the robot execute many other tasks, the proposed method seems to provide a promising way for further investigation and realization of physical human-robot interaction.

Unwilling or unable? Using three-dimensional tracking to evaluate dogs' reactions to differing human intentions.

The extent to which dogs (Canis familiaris) as a domesticated species understand human intentions is still a matter of debate. The unwilling-unable paradigm has been developed to examine whether nonhuman animals are sensitive to intentions underlying human actions. In this paradigm, subjects tended to wait longer in the testing area when presented with a human that appeared willing but unable to transfer food to them compared to an unwilling (teasing) human. In the present study, we conducted the unwilling-unable paradigm with dogs using a detailed behavioural analysis based on machine-learning driven three-dimensional tracking. Throughout two preregistered experiments, we found evidence, in line with our prediction, that dogs reacted more impatiently to actions signalling unwillingness to transfer food rather than inability. These differences were consistent through two different samples of pet dogs (total n = 96) and they were evident also in the machine-learning generated three-dimensional tracking data. Our results therefore provide robust evidence that dogs distinguish between similar actions (leading to the same outcome) associated with different intentions. However, their reactions did not lead to any measurable preference for one experimenter over the other in a subsequent transfer phase. We discuss different cognitive mechanisms that might underlie dogs' performance in this paradigm.

Can models of evolutionary transition clarify the debates over the Neolithic Revolution?

The 'Neolithic Revolution,' sometimes referred to as the emergence of agriculture at its earliest in the southern Levant, is the most significant shift in human history, shaping the world we live in today. Yet, after 100 years of study, its major cause, tempo (gradual or revolutionary), and impact of human intentionality remain disputed. Here, we examine the research potential of an evolutionary transition in individuality (ETI) to clarify this dramatic shift. Applying an ETI research perspective reveals how different causes and conditions lead to the same result, enabling a holistic view rather than a reduction of 'Neolithic' to 'agriculture,' or to one major climatic condition, inheritance system or standard evolutionary model, thus allowing us to clarify and bypass some of these heated, unresolved disputes. Additionally, unlike current archaeological emphasis on 'where,' 'when,' 'why' and 'how' questions, the ETI perspective offers a productive path for resolving a fundamental preliminary anomaly: why and how could the Neolithic lifeway evolve at all, given the selfish interest of individuals in a hunter-gatherer group? We do not intend to solve the shift to Neolithic lifeways, only to offer a fresh lens for examining it, emphasizing the relevance of tracking within and between group differences. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

The technopolitics of security: Agency, temporality, sovereignty

This introduction to the special issue on ‘the technopolitics of security’ outlines key concepts and engages debates pertaining to the relationship between techno-materiality, security governance and struggles over sovereignty. ‘Technopolitics’ refers to the strategic practice of designing and using technologies to enact political goals, producing hybrid forms of power that combine cultural, institutional and technological dimensions. These technopolitical practices give rise to new forms of agency, producing effects unintended by their designers that may alter logics of political contestation and allow technologies to be reappropriated for different political purposes. To illustrate the distributed forms of agency and contingent encounters that the technopolitics approach evokes, the article develops three key aspects of technopolitics in its relationship to security governance: (1) an understanding of agency as distributed between human and non-human actors, but also asymmetric in that human intentionality plays an assembling role that is frequently overrun by the unintended effects; (2) the temporal horizons of imagination and action over which technopolitical interventions unfold, identifying the importance of logics of anticipation and eventization; and (3) the relationship between technopolitics and sovereignty, arguing that it encourages a decentred and materialized understanding of how claims to sovereignty are made and contested.