Reviewerly/exHarmony huggingface dataset:
Learning Large Margin Multiple Granularity Features with an Improved Siamese Network for Person Re-Identification

Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods.
Integrating Turkish Wordnet KeNet to Princeton WordNet: The Case of One-to-Many Correspondences

In this paper, we introduce a novel approach of forming interlingual relations between multilingual wordnets. We have mapped Turkish senses in KeNet with their corresponding senses in Princeton WordNet by drawing one-to-many correspondences. As a result of language-specific properties, one synset in one language is matched with multiple synsets in the other language in some cases. Our method of integrating KeNet into a multilingual network also included mapping the most frequent 5000 senses in English with their equivalent senses in Turkish. What we demonstrate is that one-to-many interlingual correspondances are necessary to include in mappings both from Turkish-to-English and English-to-Turkish. Furthermore, one-to- many mappings give us insights into the semantic relations to be constructed in Turkish, such as hypernymy.
Bi-SAN-CAP: Bi-Directional Self-Attention for Image Captioning

In a typical image captioning pipeline, a Convolutional Neural Network (CNN) is used as the image encoder and Long Short-Term Memory (LSTM) as the language decoder. LSTM with attention mechanism has shown remarkable performance on sequential data including image captioning. LSTM can retain long-range dependency of sequential data. However, it is hard to parallelize the computations of LSTM because of its inherent sequential characteristics. In order to address this issue, recent works have shown benefits in using self-attention, which is highly parallelizable without requiring any temporal dependencies. However, existing techniques apply attention only in one direction to compute the context of the words. We propose an attention mechanism called Bi-directional Self-Attention (Bi-SAN) for image captioning. It computes attention both in forward and backward directions. It achieves high performance comparable to state-of-the-art methods.
Cloud Computing Adoption Strategic Planning Using ROCCA and TOGAF 9.2: A Study in Government Agency

Cloud computing is the latest paradigm that many private and public agencies have shown a significant interest in enabling the technology. However, there has been no standardize framework that support the need while aligning with business strategy. This study focuses on designing architecture framework that can help government agencies in adopting cloud solution based on ROCCA and TOGAF 9.2. Both of them are well known used for cloud adoption initiatives and architecture work, especially for organizations who are actively transforming themselves. We mapped ROCCA and TOGAF 9.2 processes to provide baseline requirements of cloud adoption. Then, validate the proposed framework with implementation and evaluation in case study for The Special Task Force of Upstream Oil and Gas Activities (SKK Migas). The result showed the processes provided are a good guidance and appropriate to support strategic government approach in analyzing and planning cloud solutions effectively.
Using Enterprise Architecture Models for Creating the Record of Processing Activities (Art. 30 GDPR)

The record of processing activities (RPA) is a central document in demonstrating compliance with the General Data Protection Regulation (GDPR). Article 30 of the GDPR specifies the information that has to be made available to the supervisory authority upon request. Currently, data protection management experts conduct their own data collection and maintain isolated RPAs. We show how existing Enterprise Architecture models can be augmented with the necessary information to maintain and generate an RPA. We evaluate the completeness and usefulness of the approach together with data protection management experts.
Deep learning based estimation of facial attributes on challenging mobile phone face datasets

Facial attribute analysis is an important step in many biometric algorithms, including face recognition based human authentication. Detecting the state of face attributes is becoming even more popular in mobile based applications, where a biometric authentication application is used to quickly and accurately verify the claimed identity of the owners device, and also, keep the device secure if an intruder attempts to gain unauthorized access to it. While there is a large number of facial attributes that can be automatically detected to support a face recognition system, in this paper we focus on detecting three specific ones that are useful during both an enrollment and authentication process: (1) determining whether the eyes of a subject are open or closed, (2) determining whether a subject is wearing glasses or not, and, finally, (3) detecting whether a subjects facial pose is either frontal or non-frontal. These attributes are associated with face image quality control, which is a very useful component of modern FR systems under the following context: during live authentication, by limiting low quality face image data, we can enhance the face-based authentication accuracy, while at the same time improve user satisfaction via improved system efficiency (i.e. less false match attempts during authentication). Thus, to automatically and efficiently detect all of the aforementioned facial attributes, we developed both conventional and deep learning based models. These models are trained and tested on diverse and challenging face image datasets, using data captured from traditional cameras and mobile devices, when operating at multiple standoff distances, in either indoor or outdoor conditions. Our proposed attribute-specific detection models are robust, yielding up to 100% accuracy (in terms of F1 score) depending on the attribute tested, as well as the model and dataset(s) used for training and testing.
Usability: Adoption, Measurement, Value

We searched for the application of usability in the literature with a focus on adoption, measurements employed, and demonstrated value. Five human factors domains served as a platform for our reflection, which included the last 20 years.As usability studies continue to accumulate, there has been only a little past reflection on usability and contributions across a variety of applications. Our research provides a background for general usability, and we target specific usability research subareas within transportation, aging populations, autistic populations, telehealth, and cybersecurity."Usability" research was explored across five different domains within human factors. The goal was not to perform an exhaustive review but, rather, sample usability practices within several specific subareas. We focused on answering three questions: How was usability adopted? How was it measured? How was it framed in terms of value?We found that usability is very domain specific. Usability benchmarking studies and empirical standards are rare. The value associated with improving usability ranged widely-from monetary benefits to saving lives. Thus, researchers are motivated to further improve usability practices. A number of data collection and interpretation challenges still call for solutions.Findings offer insight into the development of usability, as applied across a variety of subdomains. Our reflection ought to inform future theory development efforts. We are concerned about the lack of established benchmarks, which can help ground data interpretation. Future research should address this gap in the literature. We note that our findings can be used to develop better training materials for future usability researchers.
Methodology of hierarchical collision avoidance for high‐speed self‐driving vehicle based on motion‐decoupled extraction of scenarios

Collision avoidance is an important requirement for self-driving systems, particularly in high-speed scenarios, where a multi-state coupled motion makes it difficult to simultaneously reach the required accuracy, efficiency, and universal feasibility for different obstacle-avoidance behaviour. For a coupled multi-state complexity, a hierarchical collision-avoidance strategy is proposed that refines the requirements for travelling under such a scenario into two levels, general and special. At the general level, the moving elliptical contour of the subject vehicle is regularised as a settled circle through a projective transformation, which attempts to determine the subject-motion-decoupled scenario. Throughout the transformation, all positional relationships between the subject and the object vehicles are retained using invariants. At the special level, a group of relative critical collision trajectories is achieved through a feature-distance-based multi-dimensional geometric optimisation model. Under the motion-decoupled scenario, a precise collision avoidance condition is constructed by mathematically expressing the relative critical collision trajectory group using a parameterised spatio-temporal curvilinear interpolation model, which provides a reasonable safety redundancy and trajectory domain to ensure both the efficiency and accuracy of the computation. In a simulation, planning trajectories using this collision-avoidance strategy is adaptive for different collision-avoidance behaviour and are more efficient than those of other algorithms.
Low Power Embedded Gesture Recognition Using Novel Short-Range Radar Sensors

This work proposes a low-power high-accuracy embedded hand-gesture recognition using low power short-range radar sensors. The hardware and software match the requirements for battery-operated wearable devices. A 2D Convolutional Neural Network (CNN) using range frequency Doppler features is combined with a Temporal Convolutional Neural Network (TCN) for time sequence prediction. The final algorithm has a model size of only 45723 parameters, yielding a memory footprint of only 91kB. Two datasets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20210 gesture instances. On the 11 hands, gestures and an accuracy of 87% (26 users) and 92% (single user) have been achieved. Furthermore, the prediction algorithm has been implemented in the GAP8 Parallel Ultra-Low-Power processor by GreenWaves Technologies, showing that live-prediction is feasible with only 21mW of power consumption for the full gesture prediction neural network.
Application Research of Face Recognition Technology in Smart Campus

Abstract Promoting the degree of campus informationization has become an important part of the construction of smart campus. Face recognition, with its uniqueness and lifelong invariance, has natural security advantages and practical advantages in the application of smart campus, but it also has the disadvantages of high equipment cost and data leakage risk. The face recognition technology can be used to build safe educational environment, create convenient business processes and develop personalized technology service in the construction of smart campus. This paper designs and implements a dormitory management system based on face recognition. The test results show that the use of face recognition technology as a source of information collection for people entering and leaving dormitory area greatly improves the management efficiency and ensures the accuracy and reliability. The application of face recognition in campus management system will break the traditional mode of teachers and students’ work and life, and become an important part of smart campus.
Robust Dynamic CPU Resource Provisioning in Virtualized Servers

We present robust dynamic resource allocation mechanisms to allocate application resources meeting Service Level Objectives (SLOs) agreed between cloud providers and customers. In fact, two filter-based robust controllers, i.e., <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> filter and Maximum Correntropy Criterion Kalman filter (MCC-KF), are proposed. The controllers are self-adaptive, with process noise variances and covariances calculated using previous measurements within a time window. In the allocation process, a bounded client mean response time ( <inline-formula><tex-math notation="LaTeX">$\mathop {\mathrm{mRT}}$</tex-math></inline-formula> ) is maintained. Both controllers are deployed and evaluated on an experimental testbed hosting the RUBiS (Rice University Bidding System) auction benchmark web site. The proposed controllers offer improved performance under abrupt workload changes, shown via rigorous comparison with current state-of-the-art. On our experimental setup, the Single-Input-Single-Output (SISO) controllers can operate on the same server where the resource allocation is performed; while Multi-Input-Multi-Output (MIMO) controllers are on a separate server where all the data are collected for decision making. SISO controllers take decisions not dependent to other system states (servers), albeit MIMO controllers are characterized by increased communication overhead and potential delays. While SISO controllers offer improved performance over MIMO ones, the latter enable a more informed decision making framework for resource allocation problem of multi-tier applications.
The Importance of Intersection Disambiguation for Virtual Hand Techniques

Some of the most widely used selection techniques for extended reality (XR) are based on virtual hand interactions. Many existing XR frameworks provide this functionality by default; however, their implementation can differ in slight, but important ways. When preparing to make a selection with a virtual hand technique, a user's desired selection can potentially be ambiguous due to multiple intersections. Systems with varying underlying virtual hand implementations may yield contrasting selections due to resolving multiple intersections differently. This is particularly an issue when objects are smaller in size than the virtual hand representation and in dense environments. To demonstrate the importance of these differences, we present a virtual hand selection study comparing three methods that are currently used in popular XR frameworks for disambiguating selections: Closest Intersected, First Intersected, and Last Intersected. The results of our study show that the Closest Intersected method affords significantly faster selections, significantly fewer incorrect and missed selections, and yields significantly better effective throughput than the other two methods. These results show that using a framework's built-in selection technique can significantly affect an XR application's usability.
Designing for diversity in Aboriginal Australia

Aboriginal Australians have been colonized for over 230 years. As a result, many have been disconnected from their communities and identity. This paper reports on a national-scale HCI project that aims to design technology that allows Aboriginal Australians to reconnect with their communities and to reaffirm their Aboriginal identity. Our project faces significant challenges, some due to the effects of colonization and some due to the great (and under-recognized) diversity of Aboriginal Australia. In this paper, we report the design phase of our project, and discuss some of these challenges we faced. Through this, we offer insights for HCI designers and researchers undertaking similar work.
Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]

Convolutional neural networks (CNN) are widely used deep learning frameworks and are applied in the field of face recognition, achieving outstanding results. The Macropixel comparison approach is a shallow mathematical approach that recognizes faces by comparing the original pixel blocks of face images. In this article, the authors are inspired by ideas of the currently popular deep neural network framework and introduce two features into the mathematical approach: deep overlap and weighted filter. The aim is to explore if the idea of deep learning could benefit mathematical recognition method, which might extend the scope of face recognition research. Results from the experiments show that the new proposed approach achieves markedly better recognition rates than the original macropixel methods.
Trajectory Planning for Seven-DOF Robotic Arm Based on Quintic Polynormial

Trajectory planning ensures that the robot arm moves smoothly and quickly to the target position. The difficulty is to use inverse kinematics to convert the path point into the joint angle and fit a smooth function to each joint. This paper studies the polynomial trajectory planning of the seven-DOF cooperative robot KUKA LBR iiwa. Kinematics of robot arm is analyzed in the coordinate system established by denavit-hartenberg method. In the joint space, the trajectory planning of the robot arm is performed using the cubic polynomial and the quintic polynomial function. The simulation results show that the quintic polynomial method effectively solves the problem of acceleration discontinuity, and obtains a continuous smooth trajectory curve of each joint, which visually verifies the effect of trajectory planning and provides an efficient and feasible trajectory planning method.
Virtual Reality for the Assessment of Everyday Cognitive Functions in Older Adults: An Evaluation of the Virtual Reality Action Test and Two Interaction Devices in a 91-Year-Old Woman

Performance-based functional tests for the evaluation of daily living activities demonstrate strong psychometric properties and solve many of the limitations associated with self- and informant-report questionnaires. Virtual reality (VR) technology, which has gained interest as an effective medium for administering interventions in the context of healthcare, has the potential to minimize the time-demands associated with the administration and scoring of performance-based assessments. To date, efforts to develop VR systems for assessment of everyday function in older adults generally have relied on non-immersive systems. The aim of the present study was to evaluate the feasibility of an immersive VR environment for the assessment of everyday function in older adults. We present a detailed case report of an elderly woman who performed an everyday activity in an immersive VR context (Virtual Reality Action Test) with two different types of interaction devices (controller vs. sensor). VR performance was compared to performance of the same task with real objects outside of the VR system (Real Action Test). Comparisons were made on several dimensions, including (1) quality of task performance (e.g., order of task steps, errors, use and speed of hand movements); (2) subjective impression (e.g., attitudes), and (3) physiological markers of stress. Subjective impressions of performance with the different controllers also were compared for presence, cybersickness, and usability. Results showed that the participant was capable of using controllers and sensors to manipulate objects in a purposeful and goal-directed manner in the immersive VR paradigm. She performed the everyday task similarly across all conditions. She reported no cybersickness and even indicated that interactions in the VR environment were pleasant and relaxing. Thus, immersive VR is a feasible approach for function assessment even with older adults who might have very limited computer experience, no prior VR exposure, average educational experiences, and mild cognitive difficulties. Because of inherent limitations of single case reports (e.g., unknown generalizability, potential practice effects, etc.), group studies are needed to establish the full psychometric properties of the Virtual Reality Action Test.
Understanding the Gap between 2D and 3D Skeleton-Based Action Recognition

Large volumes of RGB video data are recorded and processed every day. One of the embedded data modality within these videos is the information about human motions. Up to now, this information has been almost unfeasible to extract, and thus human-motion understanding research has been mainly limited to 3D skeleton data captured by dedicated hardware only. However, with recent advances in computer vision, it is possible to estimate 2D skeleton sequences from ordinary videos quite accurately. Such 2D skeleton data possess an excellent potential for future motion understanding applications. In this paper, we adopt a state-of-the-art bidirectional LSTM network to analyze the accuracy gap in the expressive power of 2D and 3D skeleton data recorded simultaneously on a high number of 20k human actions. We further examine how the missing depth information and fluctuations in 2D skeleton sizes influence the recognition rate. We also demonstrate the suitability of 2D skeleton data for general daily activity recognition by reporting baselines on the PKU-MMD dataset.
Probabilistic Mixture Differential Cryptanalysis on Round-Reduced AES

At Eurocrypt 2017 the first secret-key distinguisher for 5-round AES has been presented. Although it allows to distinguish a random permutation from an AES-like one, it seems (rather) hard to exploit such a distinguisher in order to implement a key-recovery attack different than brute-force like. On the other hand, such result has been exploited to set up a new (competitive) secret-key distinguisher for 4-round AES, called "Mixture Differential Cryptanalysis". In this paper, we combine this new 4-round distinguisher with a modified version of a truncated differential distinguisher in order to set up a new 5-round distinguisher, that exploits properties which are independent of the secret key, of the details of the S-Box and of the MixColumns matrix. As a result, while a "classical" truncated differential distinguisher exploits the probability that a pair of (two) texts satisfies or not a given differential trail independently of the others pairs, our distinguisher works with sets of $$N\gg 2 $$ (related) pairs of texts. In particular, our new 5-round AES distinguisher exploits the fact that such sets of texts satisfy some properties with a different probability than for a random permutation. Even if such 5-round distinguisher has a higher complexity than e.g. the "multiple-of-8" one present in the literature, it can be used as starting point to set up the first key-recovery attack on 6-round AES that exploits directly a 5-round secret-key distinguisher. The goal of this paper is indeed to present and explore new approaches, showing that even a distinguisher like the one presented at Eurocrypt – believed to be hard to exploit – can be the starting point for new secret-key distinguishers and/or key-recovery attacks.