Reviewerly/exHarmony huggingface dataset:
Autonomous Group Detection, Delineation, and Selection for Human-Agent Interaction

If a human and a robot team need to approach a specific group to make an announcement or delivery, how will the human describe which group to approach, and how will the robot approach the group? The robots will need to take a relatively arbitrary description of a group, identify that group from onboard sensors, and accurately approach the correct group. This task requires the robot to reason over and delineate individuals and groups from other individuals and groups. We ran a study on how people describe groups for delineation and identified the features most likely used by a person. We then present a framework that allows for an agent to detect, delineate, and select a given social group from the context of a description. We also present a group detection algorithm that works on a mobile platform in real-time and provide a formalization for a Group Selection Problem.
A Wide 2D Median Filter for GPU Devices

The median filter is a technique of nonlinear digital filter, often used to reduce some kinds of image noise. However, in its original form it is limited in part by its algorithmic complexity, which has led to faster algorithm versions. Still, these variants are not completely able to use the modern hardware such as its parallel computing capabilities and diverse compute architectures, leaving room for accelerations. It is therefore proposed a parallel execution modification of one of these variants to obtain faster processing speed with bigger filter sizes and execution in different processing environments.
Evaluation of the effects of a virtual reality game for anti-drug awareness and prevention in Taiwan

The spread of illegal drugs use has caused numerous societal problems. There is an urgent need to enhance people awareness of the harms caused by drugs and resistance against drugs use. In order to prevent drugs abuse, nationwide anti-drug campaigns were launched to enhance and promote anti-drug propaganda in Taiwan. However, the traditional lecture-based instructional approach makes anti-drug boring and becomes less attractive. A promising and alternative approach was to make the anti-drug campaign more interesting and effective. In this study, we applied the virtual reality technology for the public to increase their knowledge about illegal drugs and consequence of drug use, to strengthen commitment to deny drug use and to enhance the attention and awareness of drugs. Thirty-three volunteers with an average age from 15 to 60 participated in the study in Taiwan. The results of this study revealed that the ease of use and usefulness influenced individual's flow experience and the flow experience is the strongest determinant of individual's satisfaction of the anti-drug virtual reality promotion.
Anomaly Detection in Wireless Body Area Network using Mahalanobis Distance and Sequential Minimal Optimization Regression

Wireless Body Area Network (WBAN) is a health service that consists of small sensors attached to the patient's body to improve health services. Apart from the importance and necessity of the WBAN, there are some problems in WBAN such as inaccurate measurements, hardware failures, discharged sensors, and sensors running out of energy which can cause false alarms and reduce confidence in using the WBAN system for remote health services. This research aims to create a system or modelling to solve anomalous data problems in WBAN health data. The system will process data to produce an analysis of information in an anomalous condition by applying the statistical method using Mahalanobis Distance and the prediction method using SMOreg. The process of collecting data and pre-processing is carried out before completing the detection of anomalous features. The data normalization process is carried out to make the process faster. This approach was tested on a real health dataset on the WBAN sensor. The results of the implementation show that the method produced a high-performance detection ratio (DR), which is above 80%. In addition, the experiment resulted that the size of the sliding window affected the anomaly detection results.
Evaluating the Performance of Hybrid Quantum-Classical Convolutional Neural Networks on NISQ Devices

The increase in quantum algorithm development in recent years has spurred the development of new algorithms which outsource classically intractable portions of an algorithm to a quantum computer. These hybrid quantum-classical algorithms must be thoroughly studied and analyzed to determine their viability. One such algorithm is the hybrid quantum-classical convolutional neural network. In this paper, we compared different implementations of hybrid quantum-classical convolutional neural networks and their fully classical counterparts. In particular, we compared their accuracy and loss performance over time. To do this, we implemented both the classical and hybrid quantum-classical algorithms with varying convolutional layers and ran them on a cluster of classical computers and IBM’s cloud quantum computers using the low-dimensional Tetris dataset. We found that hybrid quantum-classical convolutional neural networks do not outperform their classical counterparts in terms of accuracy and loss over time when tested on current-state quantum computers, which conflicts with past work. We can attribute the discrepancy to the noise present in current quantum computers affecting the results of the circuit.Nagan, Tristan Ajoodha, Ritesh
Towards Ontology-Based Design Science Research for Knowledge Accumulation and Evolution

The potential of design science research (DSR) to contribute to real-world problems solving and innovation has been considered as an opportunity for IS researchers to demonstrate the relevance and significance of DSR paradigm. While most DSR studies have been informed on single design and development projects, future research needs to consider knowledge sharing and accumulation across multiple projects. This paper argues for combining the forces of design science research and ontology studies to foster knowledge creation and evolution. We propose a new approach to DSR by adopting ontology engineering as a knowledge sharing mechanism in which researchers assemble knowledge parts throughout the study. We develop a framework for understanding, conducting and evaluating ontology-based design science research, then present the roadmap and guidelines for its conduct and evaluation. This paper concludes with a call for a more collaborative endeavor to design studies in IS research. Full paper available at http://hdl.handle.net/10125/60011
The Video Captioning Method Based On The Spatial- Temporal Information and Attention Mechanism

In order to utilize the complementarity of different level features in different regions of videos effectively, and improve the accuracy of text description in videos, we propose a video captioning method based on the spatiotemporal information and attention mechanism. First, the Faster-RCNN and VGG-16 networks are used to extract the high-level features of the interesting regions and significant targets in the video frames, respectively. Second, combing the low-level dense traj ectory features of video frames to represent the spatial appearance feature of a video. Finally, we adopt the long short-term memory network to extract the temporal association features between frames and texts. In the hidden layer of the long short-term memory, an adaptive attention mechanism is introduced to enhance the representation of the salient features for video frames, and then the text description is generated automatically. The experimental results show that our proposed method can effectively improve the accuracy of video captioning, and its performance is better than several the state-of-the-art approaches.
On the Importance of Encrypting Deep Features

In this study, we analyze model inversion attacks with only two assumptions: feature vectors of user data are known, and a black-box API for inference is provided. On the one hand, limitations of existing studies are addressed by opting for a more practical setting. Experiments have been conducted on state-of-the-art models in person re-identification, and two attack scenarios (i.e., recognizing auxiliary attributes and reconstructing user data) are investigated. Results show that an adversary could successfully infer sensitive information even under severe constraints. On the other hand, it is advisable to encrypt feature vectors, especially for a machine learning model in production. As an alternative to traditional encryption methods such as AES, a simple yet effective method termed ShuffleBits is presented. More specifically, the binary sequence of each floating-point number gets shuffled. Deployed using the one-time pad scheme, it serves as a plug-and-play module that is applicable to any neural network, and the resulting model directly outputs deep features in encrypted form. Source code is publicly available at https://github.com/nixingyang/ShuffleBits.
Online Multi-Object Tracking with Pose-Guided Object Location and Dual Self-Attention Network

The recent trend in Multi-Object Tracking (MOT) is heading towards using deep learning to detect objects and extract features. Although tracking frameworks using detection network have achieved outstanding performance in object locating on MOT, it is still challenging for crowded occlusion. In this paper, we propose to alleviate this difficulty by combining bounding boxes from outputs of both object detection and pose estimation. The motivation behind generating redundant candidates is that object detection and pose estimation can complement each other in tracking scenes. In order to get optimal tracking objects from candidates, we present Soft-Pose-NMS. For similarity calculation, we design a Dual Self-Attention Network (DSAN) with the self-attention mechanism. The network generates the self-attention map that enables the network to focus on the object area of detection and tracklet images. Simultaneously, the network can extract the temporal self-attention feature map to suppress noisy images in the tracklet. Experiments are conducted on the MOT benchmark datasets. Results show that our tracker achieves competitive results and is state-of-the-art in half of the metrics.
A competência em informação no Instituto Casa Cleber Teixeira: dimensões aplicadas na organização e representação do acervo

This article addresses the research carried out at the Casa Cleber Teixeira Institute, which aims to understand if the Information Literacy, according to the dimensions proposed by Vitorino and Piantola (2009), is present in the activities and processes carried out regarding the organization and representation of the collections of this institution, as well as in the restructuring of the place to accommodate the applied changes. It is a qualitative and exploratory research, which, through bibliographic and documentary research, demonstrates which organizational processes are part of the organization of the collections and whether they were applied based on the Information Literacy. In the research results, it was noticed that the Casa Cleber Teixeira Institute values the proper organization of the place, applying the Information Literacy dimensions in their organizational processes. Thus, it is concluded that the cultural space understands what is necessary to improve its structural organization and its collections, and how much Information Literacy is crucial for the better adaptation of the place and its organization, enabling the access to your materials and the promotion of your space in an optimized way.
Trusted Internet of Things Based on Trusted Computing

Thanks to the rapid development of mobile Internet, the application and popularity of the IoT are becoming wider and wider. However, new security threats and attacks are constantly emerging, IoT has the characteristics of massive terminal devices, diversification and intelligence of devices. Therefore, it becomes a hot research direction to ensure the security and trustworthiness of the IoT computing environment today. Trusted IoT applies trusted computing technology to IoT scenarios to ensure trustworthiness. The software and hardware resources are limited in IoT scenario, such that low power consumption and availability are required. Therefore, the design and implementation of a trusted IoT will confront many security challenges. This paper summarizes the research ideas and states of the trusted IoT from three aspects: the lightweight root of trust system, software attestation, and secure code update, and introduces representative research results for trusted IoT.
Optimization based 3D Multi-Object Tracking using Camera and Radar Data

Robust and reliable online 3D multi-object tracking is an essential component of autonomous driving. Recent research follows the tracking-by-detection paradigm and focuses mainly on lidar sensors, due to their superior range, resolution and depth accuracy compared to other automotive sensors. This simplifies the challenging data association in crowded urban road scenes, resulting in a predominant status of laser based methods. In contrast, we propose an online 3D multi-object tracker based solely on mono camera images and radar data to promote non-lidar based tracking research. By representing all detections of one frame as a Gaussian mixture model (GMM), we are able to avoid a fixed data association, which may include wrong assumptions. Instead, we assign the GMM to each tracked object and solve the data association implicitly and jointly by estimating the full 3D object tracks in our factor graph based optimization back end. By including all available information from the object detector, our algorithm achieves accurate, robust and reliable tracking results. We conduct real world experiments on the nuScenes tracking data set improving the state-of-the-art for non-lidar based methods from 17.7% to 34.1 % AMOTA.
OrBit: OR-Proof Identity-Based Identification with Tight Security for (as Low As) 1-Bit Loss

Tightening the security reduction of a cryptosystem involves reducing the advantage of an adversary breaking the cryptosystem to a security assumption as closely as possible. Tighter security on a cryptosystem shows a clearer picture of its security, allowing for a more optimal security parameter at a certain level. In this work, we propose techniques to tighten the security of identity-based identification (IBI) schemes and demonstrate promising new results compared to existing reduction bounds. We show two distinct transformations for tightening security against concurrent attackers via the OR-proof technique of Fujioka et al. to lower security reduction loss. Our proposed techniques produce tighter security guarantees for as low as only a one-bit loss bound, hence the name: OrBit.
The Concept of Utility: The Role of Utilitarianism in Formation of a Technological Worldview

An attempt has been made to consider the ways in which the concept of “utility” is defined in modern utilitarianism and their role in the development of a technological worldview. It took two hundred years of creativity to form the modern understanding of utility. By defining the concept of utility “utilitarianly” as a way of existence of a social system supported by additional sources of survival and the benefit for all, we are able to understand utility from the technical point of view as a rational balance of means and goals. In this regard, the use of the method of historical reconstruction of the essence of the concept of “utility” becomes important for study. Based on the works of utilitarians and representatives of the philosophy of technology, we carry out a historical reconstruction of the “utility” concept in an attempt to clarify its ontological foundations and come to the understanding of utility as a multifaceted phenomenon, unthinkable without social reality, but developing in the direction to the new types and levels of existence. It is aimed at reducing the distance between what is desired and what is due, being and reasoning, and achieving happiness as a form of spiritual balance. The degree of happiness is determined by the principle of utility, which should be understood as a set of rational methods that are successfully implemented for effective solution of technical problems and as a drive for innovations, through which humans are able to expand the scope of possible experience and transform the environment without harming themselves and society.
Small target modified car parts detection based on improved Faster-RCNN

Because the small target image has fewer pixels, it is prone to miss detection and error detection; the target detection algorithm needs to be improved and enhanced to deal with the problem about the small target detection in specific scenarios. In this paper, small target detection algorithm is applied to the new field of illegally modified vehicle detection to reduce the workload of traffic management. The commonest modification is to install a rear wing and change the contact angle between the hub and the ground. This paper proposes a method to detect modified car parts based on improving Faster-Rcnn, in order to detect two modifications described above. On the basis of the original Faster-Rcnn, using multi-scale training and increasing the number of Anchors to enhance the robustness of the network in detecting targets of different sizes, and introducing the Soft-NMS algorithm to replace the NMS algorithm, to solve the problem of partial overlap between two targets when the distance is close, the possibility of missed detection of targets with low confidence and bounding boxes with high confidence scores are not always more reliable than bounding boxes with low confidence. Experiments show that compared with the original Faster-Rcnn, the detection accuracy is increased by 4.6%, and the model has a certain generalization ability and robustness.
Mathematical and physical techniques of modeling and simulation of pattern recognition in the stock market

Abstract The following article presents the analysis through mathematical and physical techniques of large databases, which are very common today, due to the large number of variables (especially in the information and physics industry) and the amount of information that results from a process, therefore an analysis is necessary that allows the Decision in a responsible manner, looking for scientific criteria that support said decisions, in our case a database of the forex system will be taken. Initially, a study and calculation of different measurements between the samples and their characteristics will be carried out to make a good prediction of the data and their behavior using different classification methods inspired by basic sciences. Below is an explanation of the techniques based on the analysis of data components and the correlations that exist between the variables, which is a technique widely used in physical processes to determine the correlations between variables.
The Sounds of Togetherness

When people live in a home with others, they feel connected through passive, subtle signs of togetherness. Feeling co-present and connected over distance is a challenge, especially when living across different timezones. A key challenge when designing for communication over timezones is the difficulty of arranging synchronous communication, which makes traditional methods like phone conversations or video calls difficult. Additionally, the richness of passive, ambient togetherness is lost through these communication methods. We investigate the role of sounds in passive togetherness in co-located homes, and speculate about their implications for asynchronous design for togetherness over distance.
English Translation Teaching Model of Computer Multimedia System Under the Background of Big Data

With the development and prosperity of economic globalization, translation, as a means of information transmission in intercultural communication, is playing an increasingly important role, computer-assisted English teaching is both a normal and a trend. This paper mainly studies the English translation teaching model of computer multimedia system under the background of big data. In this paper, a computer aided translation course is designed. This course includes the determination of course objectives, the organization of course contents and the design of course implementation methods. In this paper, six students from a certain university are selected as experimental subjects to carry out an experiment on translation teaching assisted by computer multimedia system. The experimental results show that the computer multimedia assistance can effectively improve the performance of translation.
Features of Spatial-Temporal Hierarchical Structures Formation

The degree of ordering of the structure of technologically important materials formed as a result of the evolution of complex physicochemical systems determines their physical properties, in particular optical. In this regard, the primary task for the theoretical study of methods for obtaining materials with predetermined physical properties is to develop approaches to describe the evolution of fractal (scale-invariant) objects in the formation of self-similar structures in systems exhibiting chaotic behavior. The paper forms an idea of the processes of evolution in materials formed as a result of stochastic processes. It is established that the conduct of ultrametrics in time space allows to characterize the time of the evolutionary process of fractal dimension, which is calculated either theoretically or model. The description of evolutionary processes in a condensed medium, accompanied by topological transformations, is significantly supplemented by the method of describing the stages of evolution of structures, which makes it possible to analyze a wide range of materials and can control their properties, primarily optical. It is shown that the most large-scale invariant structures, due to the investigated properties, can be used as information carriers. It is demonstrated that the presence in physical systems of fractal temporal dimension and generates a self-similar (consisting of parts in a sense similar to the whole object) evolutionary tree, which, in turn, generates spatial objects of non-integer dimension, observed in real situations. On the other hand, temporal fractality provides analysis of systems with dynamic chaos, leading to universal relaxation functions. In particular, in systems with a large-scale invariant distribution of relaxation characteristics, an algebraic law of relaxation is manifested, which leads to rheological models and equations of states, which are characterized by fractional derivatives. It is argued that the fractal dimension of time hierarchies stores information that determines the process of self-organization. Developed in the paper ideas about the processes of building the structure of materials, which lead to the fractal geometry of objects, can be used to predict their properties, in particular, optical.