Reviewerly/exHarmony huggingface dataset:
UnifiedFace: A Uniform Margin Loss Function for Face Recognition

Face recognition has achieved great success due to the development of deep convolutional neural networks (DCNNs) and loss functions based on margin. However, complex DCNNs bring a large number of parameters as well as computational effort, which pose a significant challenge to resource-constrained embedded devices. Meanwhile, the popular margin-based loss functions all introduce only one type of margin and cannot further introduce a larger margin to achieve tighter classification boundary. In contrast to the common approach, we believe that additive and multiplicative margins should be used jointly to introduce larger margins from the margin perspective. Therefore, we propose a new margin-based loss function called UnifiedFace. First, we introduce an additive margin in the target angle activation function. Second, we add a multiplicative margin in the non-target angle. UnifiedFace introduces both additive and multiplicative margins, allowing for the introduction of large margins to achieve more compact intra-class variance and closer separated inter-class variance. In addition, we specifically design efficient face recognition models called GhostFaceNet for resource-constrained embedded devices. Experimental results demonstrate that UnifiedFace achieves state-of-the-art performance or performance competed with popular methods in training datasets of different sizes. UnifiedFace achieves optimal performance in models of varying complexity. Moreover, competitive results are achieved in the large-scale test set IJBB/C, especially the state-of-the-art performance achieved in TAR (FAR=1e−6). GhostFaceNet can significantly improve operational efficiency without significantly degrading recognition performance, making it ideal for embedded devices with limited resources.
Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking

The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer's, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.
Accurate 3D to 2D Object Distance Estimation from the Mapped Point Cloud Data

Distance estimation is one of the oldest and most challenging tasks in computer vision using only a monocular camera. This can be challenging owing to the presence of occlusions, noise, and variations in the lighting, texture, and shape of objects. Additionally, the motion of the camera and objects in the scene can affect the accuracy of the distance estimation. Various techniques have been proposed to overcome these challenges, including stereo matching, structured light, depth from focus, depth from defocus, depth from motion, and time of flight. The addition of information from a high-resolution 3D view of the surroundings simplifies the distance calculation. This paper describes a novel distance estimation method that operates with converted point cloud data. The proposed method is a reliable map-based bird’s eye view (BEV) that calculates the distance to the detected objects. Using the help of the Euler-region proposal network (E-RPN) model, a LiDAR-to-image-based method for metric distance estimation with 3D bounding box projections onto the image was proposed. We demonstrate that despite the general difficulty of the BEV representation in understanding features related to the height coordinate, it is possible to extract all parameters characterizing the bounding boxes of the objects, including their height and elevation. Finally, we applied the triangulation method to calculate the accurate distance to the objects and statistically proved that our methodology is one of the best in terms of accuracy and robustness.
Self-Parameterized Chaotic Map for Low-Cost Robust Chaos

This paper presents a general method, called “self-parameterization”, for designing one-dimensional (1-D) chaotic maps that provide wider chaotic regions compared to existing 1-D maps. A wide chaotic region is a desirable property, as it helps to provide robust performance by enlarging the design space in many hardware-security applications, including reconfigurable logic and encryption. The proposed self-parameterization scheme uses only one existing chaotic map, referred to as the seed map, and a simple transformation block. The effective control parameter of the seed map is treated as an intermediate variable derived from the input and control parameter of the self-parameterized map, under some constraints, to achieve the desired functionality. The widening of the chaotic region after adding self-parameterization is first demonstrated on three ideal map functions: Logistic; Tent; and Sine. A digitized version of the scheme was developed and realized in a field-programmable gate array (FPGA) implementation. An analog version of the proposed scheme was developed with very low transistor-count analog topologies for hardware-constrained integrated circuit (IC) implementation. The chaotic performance of both digital and analog implementations was evaluated with bifurcation plots and four established chaotic entropy metrics: the Lyapunov Exponent; the Correlation Coefficient; the Correlation Dimension; and Approximate Entropy. An application of the proposed scheme was demonstrated in a random number generator design, and the statistical randomness of the generated sequence was verified with the NIST test.
A Privacy-Preserving Ride Matching Scheme for Ride Sharing Services in a Hot Spot Area

Ride sharing is a service that enables users to share trips with others, conserving energy, decreasing emissions and reducing traffic congestion. Selecting a suitable partner for a user based on the their trip data is essential for the service, but it also leads to privacy disclosure, e.g., the user’s location and trajectory. Many privacy-preserving solutions for ride sharing services have been proposed, which are based on cryptographic technology and provide accurate matching services. However, these encryption-based algorithms are very complicated and difficult to calculate. In hot spots, such as stations, airports and sport gymnasiums, a large number of users may apply for a ride sharing service in short space of time, which will place huge pressure on the service provider. Using traditional matching methods increases the matching time and leads to a less favorable user experience. To solve these problems, we model them, aiming to maximize the vehicle’s carrying capacity and propose a lightweight privacy-preserving ride matching scheme for selecting feasible partners during busy periods with a large number of requests. To achieve this, we make use of the homomorphic encryption technique to hide location data and design a scheme to calculate the distances between users in road networks securely and efficiently. We employ a road network embedding technique to calculate the distance between users. Moreover, we use travel time instead of space distance, which makes matching more accurate. Further, with the encrypted itineraries of users, the service provider selects potential ride share partners according to the feasibility of time schedules. We use ciphertext packing to reduce overhead, improving the efficiency of ride matching. Finally, we evaluate our scheme with simulation and demonstrate that our scheme achieves an efficient and accurate matching service. It only takes a few seconds to complete the matching, and the matching accuracy is higher than 85 percent in most cases.
End-to-end emotional speech recognition using acoustic model adaptation based on knowledge distillation

The end-to-end approach provides better performance in speech recognition compared to the traditional hidden Markov model-deep neural network (HMM-DNN)-based approach, but still shows poor performance in abnormal speech, especially emotional speech. The optimal solution is to build an acoustic model suitable for emotional speech recognition using only emotional speech data for each emotion, but it is impossible because it is difficult to collect sufficient amount of emotional speech data for each emotion. In this study, we propose a method to improve the emotional speech recognition performance by using the knowledge distillation technique that was originally introduced to decrease computational intensity of deep learning-based approaches by reducing the number of model parameters. In addition to its use as model compression, we employ this technique for model adaptation to emotional speech. The proposed method builds a basic model (referred to as a teacher model) with a number of model parameters using an amount of normal speech data, and then constructs a target model (referred to as a student model) with fewer model parameters using a small amount of emotional speech data (i.e., adaptation data). Since the student model is built with emotional speech data, it is expected to reflect the emotional characteristics of each emotion well. In the emotional speech recognition experiment, the student model maintained recognition performance regardless of the number of model parameters, whereas the teacher model degraded performance significantly as the number of parameters decreased, showing performance degradation of about 10% in word error rate. This result demonstrates that the student model serves as an acoustic model suitable for emotional speech recognition even though it does not require much emotional speech data.
Efficient query evaluation techniques over large amount of distributed linked data

As RDF becomes more widely established and the amount of linked data is rapidly increasing, the efficient querying of large amount of data becomes a significant challenge. In this paper, we propose a family of algorithms for querying large amount of linked data in a distributed manner. These query evaluation algorithms are independent of the way the data is stored, as well as of the particular implementation of the query evaluation. We then use the MapReduce paradigm to present a distributed implementation of these algorithms and experimentally evaluate them, although the algorithms could be straightforwardly translated into other distributed processing frameworks. We also investigate and propose multiple query decomposition approaches of Basic Graph Patterns (subclass of SPARQL queries) that are used to improve the overall performance of the distributed query answering. A deep analysis of the effectiveness of these decomposition algorithms is also provided.
Enterprise data security compliance strategy: A study based on typical cases

Data security compliance management is the fundamental premise that data application does not infringe on the interests of countries, enterprises and individuals. It is an important foundation for realizing the driving role of data elements. Based on the data security compliance management practices of typical enterprises at home and abroad, this paper summarizes the excellent practices of leading demonstration enterprises, and proposes six key strategies for enterprise data security compliance, including classification of typical data application scenarios, analysis of key links of data security compliance application, formulation of data security compliance application management system and standards, optimization of data security compliance application process mechanism, formulation of whole-process management measures of data security compliance, and improvement of data security compliance guarantee mechanism, as to provide references for the research and construction of enterprise data security compliance management system.
Metaverse: A Platform for Designers

The introduction of games such as Roblox, Minecraft, and others proves that the metaverse concept has been around for a few decades.Mark Zuckerberg's recent development has broadened the meaning of the term "metaverse" and drawn more attention.To put it simply, the Metaverse is the internet in three dimensions.However, not everyone understands this simple comparison.As in the early days of the internet, not everyone understands the mechanism, the possibilities, the risks, and the limitations.This study aims to identify potential opportunities that can benefit the design field.Our research investigates the idea and concept of the Metaverse and the features and what the design field can offer in this domain.Design fields like fashion, architecture and advertising are among the potential targets.In this paper, we present the potential of the Metaverse for designers, beginning with a study of critical characteristics and features.The possible opportunity and challenges for the design field is then discussed, and finally, we conclude its future direction.
Autonomous Movement of Wheelchair by Cameras and YOLOv7

A wheelchair can provide limited but crucial mobility to an injured or disabled individual.This paper presents the first stage of the development of a smart wheelchair which is the customization of a manually controlled wheelchair with a novel implementation of octascopic vision.This relatively inexpensive design of an autonomous wheelchair consists of two monochromic camera arrays (each having four cameras) placed around the frame of the wheelchair to achieve a view of 360 degrees.The initial research goal was to design a wheelchair controlled by the embedded processor, allowing the wheelchair to navigate autonomously around an indoor facility with and without human intervention.Additionally, it was intended to allow those previously denied access to the world of automatic wheelchairs because of a low personal income.Through the testing of wheelchair functionality, (a) a large dataset of octascopic images was captured from this wheelchair, and (b) a YOLOv7-based object detection model was developed to avoid obstacles and autonomously control the movement.This paper presents the camera placement and the obstacle detection model using octascopic images.
Stability Study of a Neuro-Fuzzy Output System Based on Ratio Area Method

Purpose of research is to study the hypothesis about the possibility of changing the type of transition process during training in a neuro-fuzzy inference system based on area ratio method, and to study the properties of weight coefficient influence on its stability. Methods. An apparatus of fuzzy logic is used for the development of a neuro-fuzzy output system. At the same time, input and output variables are described by triangular membership functions. Mamdani implication model was used in the compositional rule. A linear model of area ratio was used in defasification. The reverse error propagation method was used during training. Results. In experimental studies, it was found that the proposed neuro-fuzzy model based on area ratio method allows to change the type of transition process, namely, to transform oscillatory process into an aperiodic (monotonic) process. In experimental studies, it was also found that the stability of neuro-fuzzy output system is more influenced by the weight coefficient determined in calculating the total area of membership output functions. Thus, the obtained results prove: first, that the proposed neuro-odd output system ensures the transformation of transfer characteristics, and second, ensures its stability in a given range of weight coefficient characteristics. Conclusion: The architecture of an adaptive neuro-fuzzy output system based on a linear method of area ratio is described. A distinctive feature of the proposed architecture is the use of an odd system of triangular accessory functions at inputs and outputs. Analysis of the simulation process of its training showed that it s important to ensure stability during training. It is also necessary to establish permissible values of the weight coefficient, numerical values of which in its turn affect the transformation of transfer characteristics of a neuro-fuzzy output system.
A Study of Smart Construction Based on the Digital Twins

Nowadays, with the rapid development of technology in the world, the debate over the concept of metaverse has exploded, while various new technologies, concepts and scenarios geared towards metaverse have emerged and various industries are making their presence felt.The digital twin has also become one of the core technologies for building metaverse.The digital twin, it can be argued, is the closest technology to comprehending the metaverse, and it provides the best technical support for the digitisation of architecture and cities.Originating from digital design, virtual simulation and the industrial internet, the digital twin as a concept that transcends reality is essentially a simulation process that brings together multiple dimensions and disciplines by playing with data from multiple aspects, including physical models and sensors, to map reality in virtual space, thus corresponding to the full lifecycle of an entity.This paper examines the application of the digital twin in smart construction and its advantages and disadvantages, and analyses the research topic through a case study approach, thus illustrating the significance of the digital twin for smart construction.The paper also concludes that the digital twin has the advantages of accurate mapping, interaction between reality and reality, and intelligent intervention in the construction industry, but also has certain shortcomings in terms of data awareness and software.
A Modulation Recognition Algorithm Based on Wavelet Transform Entropy and High-order Cumulant for Satellite Signal Modulation

调制识别是信号检测与解调的关键环节,针对卫星调制中采用的MAPSK,MQAM,MFSK,MPSK方式,提出了一种计算小波变换熵值并结合高阶累积量的联合调制识别算法.根据小波变换对时频信息敏感的特点,不同调制方式高阶累积量计算结果的区分性以及不同复杂度的调制信号熵值结果不同,分析了以上4类调制信号的计算结果,提出了基于小波变换熵值及高阶累积量联合的卫星信号调制识别算法.计算调制信号小波系数,据此计算熵值,实现对调制信号的类别划分,使用高阶累积量实现类别内的信号分类.经过仿真分析,可实现在8dB以上达到0.9识别率的效果,该方法对高阶(64阶调制)信号识别具有一定借鉴意义.
A User-Friendly Dashboard for Tracking Global Open Access Performance

We identify two issues that obstruct the clear assessment of open access publishing performance by academics, librarians, and other stakeholders. Firstly, the current open access categories are difficult to understand and reason about. Secondly, there are no easily accessible tools that allow many countries and institutions to view their open access publishing performance. To address the first issue, we have re-designed the open access categories using descriptive category names rather than opaque colour names, re-organised the categories into a hierarchy to facilitate top-down comprehension, and presented publisher and repository-based open access categories without preferencing one category over the other. To address the second issue, we have developed the COKI Open Access Dashboard, an open-source, public dashboard for measuring and visualising the open access performance of 189 countries and over 7000 institutions. We explore the design decisions underlying the redesigned categories and the dashboard.
Conversational Composites: A Method for Illustration Layering

The conversational nature of sketches is a widespread topic of research. Understanding drawing as a cognitive activity is commonly accepted, and many of the most extensively used methods within Human-Computer Interaction recruit sketching as a technique for ideation, explanation, documentation, and conversation. To further develop the use of this illustration process as a tool of knowledge production, we suggest a novel sketching method. We present Conversational Composites: a flexible method grounded in the material and tangible qualities of sketching in different forms and media, creating physical and digital layers of conversation between participants. We present and reflect on the proposed method through an applied case of a conversation between a PhD student and her supervisor, and offer suggestions on how it may be adapted and appropriated by other researchers in the HCI community.
Making From Home: Reflections on Crafting Tangible Interfaces for Stay-at-home Living

Pandemic lockdowns created new barriers for HCI researchers, but also provided new opportunities for deeper engagement and reflection in our home environments. Five participants were introduced with a design brief on self-isolation and engaged 12 of their friends and family in the design process of in-the-isolated-wild deployments. By analysing the design process, we found that –while ‘making from home’– our participants noticed the subtlety of the interactions and materials, the processes of remembrance embedded in craft, the use of imperfection and metaphor in homeware, and how ambient presence can provide emotional support. We then conducted a follow-up study on the benefits and limitations of using a crafting approach while ‘making from home’ and discuss the tensions that novices experience while designing TUIs in such an environment. Our results expand the literature by highlighting the benefits, limitations, and trade-offs of user-led design, DIY user empowerment, and harnessing the power of craft.
The Body as Sound: Unpacking Vocal Embodiment through Auditory Biofeedback

Multi-sensory experiences underpin embodiment, whether with the body itself or technological extensions of it. Vocalists experience intensely personal embodiment, as vocalisation has few outwardly visible effects and kinaesthetic sensations occur largely within the body, rather than through external touch. We explored this embodiment using a probe which sonified laryngeal muscular movements and provided novel auditory feedback to two vocalists over a month-long period. Somatic and micro-phenomenological approaches revealed that the vocalists understand their physiology through its sound, rather than awareness of the muscular actions themselves. The feedback shaped the vocalists’ perceptions of their practice and revealed a desire for reassurance about exploration of one’s body when the body-as-sound understanding was disrupted. Vocalists experienced uncertainty and doubt without affirmation of perceived correctness. This research also suggests that technology is viewed as infallible and highlights expectations that exist about its ability to dictate success, even when we desire or intend to explore.