Reviewerly/exHarmony huggingface dataset:
A Trajectory-Based Method for Dynamic Scene Recognition

Existing methods for dynamic scene recognition mostly use global features extracted from the entire video frame or a video segment. In this paper, a trajectory-based dynamic scene recognition method is proposed. A trajectory is formed by a pixel moving across consecutive frames of a video segment. The local regions surrounding the trajectory provide useful appearance and motion information about a portion of the video segment. The proposed method works at several stages. First, dense and evenly distributed trajectories are extracted from a video segment. Then, the fully-connected-layer features are extracted from each trajectory using a pre-trained Convolutional Neural Networks (CNNs) model, forming a feature sequence. Next, these feature sequences are fed into a Long-Short-Term-Memory (LSTM) network to learn their temporal behavior. Finally, by aggregating the information of the trajectories, a global representation of the video segment can be obtained for classification purposes. The LSTM is trained using synthetic trajectory feature sequences instead of real ones. The synthetic feature sequences are generated with a series of generative adversarial networks (GANs). In addition to classification, category-specific discriminative trajectories are located in a video segment, which help reveal what portions of a video segment are more important than others. This is achieved by formulating an optimization problem to learn discriminative part detectors for all categories simultaneously. Experimental results on two benchmark dynamic scene datasets show that the proposed method is very competitive with six other methods.
Experiments on Zero-Shot Object Detection for Indoor Robots

Zero-shot object detection is a new very challenging concept in computer vision. While zero-shot object detection is a more practical setting than traditional object detection, not many researchers have explored it, and not for specific applications like indoor robots. In this work, experiments to evaluate zero-shot object detection were conducted on images collected by a real indoor robot whereas the model was trained on a larger dataset (SUN RGB-D [1]). This work aims to evaluate this more challenging setup resulting from the difference in distribution between training and testing images.In this work, the image visual features were extracted and embedded with class labels to a common semantic space. Then, the network was trained to learn an aligning function that maps the image embedded visual features to the embedded class name, to be capable of predicting novel classes that were not presented at training time.Despite that, the currently achieved results are not very high, but this research opens the doors for further exploration of this challenging setup.
An Extended Benchmark System of Word Embedding Methods for Vulnerability Detection

Security researchers have used Natural Language Processing (NLP) and Deep Learning techniques for programming code analysis tasks such as automated bug detection and vulnerability prediction or classification. These studies mainly generate the input vectors for the deep learning models based on the NLP embedding methods. Nevertheless, while there are many existing embedding methods, the structures of neural networks are diverse and usually heuristic. This makes it difficult to select effective combinations of neural models and the embedding techniques for training the code vulnerability detectors. To address this challenge, we extended a benchmark system to analyze the compatibility of four popular word embedding techniques with four different neural networks, including the standard Bidirectional Long Short-Term Memory (Bi-LSTM), the Bi-LSTM applied attention mechanism, the Convolutional Neural Network (CNN), and the classic Deep Neural Network (DNN). We trained and tested the models by using two types of vulnerable function datasets written in C code. Our results revealed that the Bi-LSTM model combined with the FastText embedding technique showed the most efficient detection rate on a real-world but not on an artificially constructed dataset. Further comparisons with the other combinations are also discussed in detail in our result.
Research on The Significance and Specific Application Links of Computer Network Technology in Electronic Information Engineering

Electronic information engineering is a system engineering based on computer network and other modern technologies, mainly including information collection, information processing, equipment maintenance and system construction. With the continuous development of computer network technology, electronic information engineering has also made breakthrough progress. Computer network technology is mainly used in the information transmission link, information resource sharing link and information remote control link of electronic information engineering, which brings great changes and improvement to people's daily work and life.
Video person reidentification based on neural ordinary differential equations and graph convolution network

Abstract Person reidentification rate has become a challenging research topic in the field of computer vision due to the fact that person appearance is easily affected by lighting, posture and perspective. In order to make full use of the continuity of video data on the time line and the unstructured relationship of features, a video person reidentification algorithm combining the neural ordinary differential equation with the graph convolution network is proposed in this paper. First, a continuous time model is constructed by using the ordinary differential equation (ODE) network so as to capture hidden information between video frames. By simulating the hidden space of the hidden variables with the hidden time series model, the hidden information between frames that may be ignored in the discrete model can be obtained. Then, the features of the generated video frames are given to the graph convolution network to reconstruct them. Finally, weak supervision is used to classify the features. Experiments on PRID2011 datasets show that the proposed algorithm can significantly improve person reidentification performance.
Keras Convolutional Neural Network Model in Sequential Build of 4 and 6 Layer Architectures

Machines see pictures utilizing pixels. Pixels in pictures are generally related. A convolution increases a grid of pixels with a channel framework or 'part' and summarizes the duplication esteems. Convolutional Neural Network is one of the most sought over concepts of technology for Image classification. Implementing them through Keras took a step forward and initiated fast processing. In this research paper, the authors intend to explore the different architectures of the convolutional neural networks and the inline layers of the network and understand the influence of training batches and epochs on it. To make things significantly simpler to decipher, we will utilize the 'accuracy' metric to see the ac- curacy score to comprehend the likelihood of cross-entropy.
Exploiting Non-Linear Redundancy for Neural Model Compression

Deploying deep learning models with millions, even billions, of parameters is challenging given real world memory, power and compute constraints. In an effort to make these models more practical, in this paper, we propose a novel model compression approach that exploits linear dependence between the activations in a layer to eliminate entire structural units (neurons/convolutional filters). Our approach also adjusts the weights of the layer in a manner that is provably lossless while training if the removed neuron was perfectly predictable. We combine this approach with an annealing algorithm that may be applied during training, or even on a trained model, and demonstrate, using popular datasets, that our technique can reduce the parameters of VGG and AlexNet by more than 97% on CIFAR-10, 85% on Caltech-256, and 19% on ImageNet at less than 2% loss in accuracy. Furthermore, we provide theoretical results showing that in overparametrized, locally linear (ReLU) neural networks where redundant features exist, and with correct hyperparameter selection, our method is indeed able to capture and suppress those dependencies.
DBIM: An Efficient and Resilient PII Data Breach Incident Management System

This paper presents the design and development of an automated PII data breach incident management system (DBIM) as a complimentary tool for privacy management for PDPA compliance. Our DBIM possesses three key functions including incident type classification based on incident impact analysis and support vector machine (SVM), response plan generation based on rule-based system, and incident follow-up. With the proposed SVM and rule-based approach, the proposed system is efficient and resilient in handling a high volume of incident cases. To substantiate the efficiency of our proposed system, we developed a system prototype and created test cases to validate the functionality of the system.
Research on variable scale algorithm

This paper is mainly to study the calculation characteristics of different iterative matrices H(k+1) for different function types in the variable scale algorithm. After calculating and analyzing the polynomial function, non-polynomial function, and selecting different initial points for these two functions, research found that for simple polynomial functions, exponential functions, and even complex functions combined with the two Choosing any variable-scale matrix algorithm can quickly calculate the result, and it is basically not affected by the initial point selection. In the process of solving the “banana function” in the polynomial function, the DFP algorithm is optimal in terms of the number of iterations and the calculation time. Although the BFGS algorithm has better numerical stability, it is better for the “banana function” The calculation time is relatively long, and there are many iterations. When studying the logarithmic function, this article found that the performance of the “banana function” is not good, but the calculation performance of the BFGS(2) algorithm is the best. Finally, this article selected different initial points for the two functions and found that the numerical decline of the DFP algorithm is indeed unstable, while the BFGS algorithm is more stable than it, especially the BFGS(3) algorithm, which has the smallest change.
Multiple-Input Multiple-Output Fusion Network for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) has attracted considerable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes. Most of the existing methods attempt to find a mapping from visual space to semantic space, such mapping can easily result in the domain shift problem. To address this issue, we propose a Multiple-Input Multiple-Output Fusion Network to GZSL. It can generate similar common semantic representation to paired inputs even with only the class semantic embeddings. This makes it possible to synthesize pseudo samples from attributes of unseen classes. Extensive experiments carried out on three benchmark datasets show the effectiveness of the proposed model.
Open-World Group Retrieval with Ambiguity Removal: A Benchmark

Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. This paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. The open-world scenario considers that there are probably non-target people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. The open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.
Boosting OLTP Performance Using Write-Back Client-Side Caches

We present a comprehensive evaluation of client-side caches to enhance the performance of MySQL for online transaction processing (OLTP) workloads. We focus on TPC-C benchmark and the write-back policy of the client-side cache. With this policy, the cache processes all application writes and propagates them to its data store asynchronously. We observe with 1 TPC-C warehouse, the cache enhances performance of MySQL InnoDB by 70%. The cache scales horizontally as a function of the number of warehouses and servers to boost TPC-C’s tpm-C by factors as high as 25 folds with 100 warehouses and 20 servers. The main limitation of the cache is its requirement for an abundant amount of memory. We extend the cache with a transaction processing storage manager (Berkeley DB, BDB) to minimize its amount of required memory, quantifying its impact on TPC-C’s tpm-C. We detail two variants, Async-BDB and Sync-BDB. The slowest variant, Sync-BDB, continues to scale horizontally to boost TPC-C performance by more than 13 folds with 100 TPC-C warehouses.
Optimized Page Ownership Management for Guaranteeing the SLO of Buffered Writes

Modern cloud platforms adopt Cgroups to efficiently utilize system resources and guarantee service-level objective (SLO). However, Cgroups cannot achieve SLO guarantee, when multiple Cgroups instances share data in the page cache. In this paper, we reveal that this limitation originates from the incomplete page ownership management of conventional Cgroups and suggest an optimized ownership management scheme. Our scheme accurately identifies the ownership information of each page in the page cache, and utilizes it in controlling I/O resources. Evaluation results show that, unlike the conventional scheme, our scheme effectively guarantees SLO of the Cgroups instances in terms of I/O throughput.
A Comparison Study on Infant-Parent Voice Diarization

We design a framework for studying prelinguistic child voice from 3 to 24 months based on state-of-the-art algorithms in diarization. Our system consists of a time-invariant feature extractor, a context-dependent embedding generator, and a classifier. We study the effect of swapping out different components of the system, as well as changing loss function, to find the best performance. We also present a multiple-instance learning technique that allows us to pre-train our parameters on larger datasets with coarser segment boundary labels. We found that our best system achieved 43.8% DER on test dataset, compared to 55.4% DER achieved by LENA software. We also found that using convolutional feature extractor instead of logmel features significantly increases the performance of neural diarization.
Human Resources Management Performance’s Effect on Organizational Commitment: An Applied Study Based on A Sample of Faculty Members’ Opinions at Al-Muthanna University / Faculty of Administration and Economics

The aim of the current study is to test the impact of human resource management performance represented by (achievement, service provided, participation, and job satisfaction) In the organizational commitment represented by (Normative commitment, Emotional commitment, and Continuous commitment) of a group of teaching staff members at Al-Muthanna University College of Administration and Economics.The importance of the fundamental study is reflected in finding out to what extent does human resources management performance effect on the organizational commitment shown by members of the studied sample. (60) Questionnaires were distributed, and after tabulating the data, only (51) questionnaires were found suitable for analysis. The researcher used a series of statistical methods to extract the required results. Standard deviation, stability coefficient, correlation coefficient using (SPSS .V.25) program, and effect coefficients using (AMOS .V.25) program.Consequently, the study reached a set of results, the most important of which is reflected in the existence of a statistically significant correlation and impact relationship between human resource performance management and organizational commitment, this indicates the necessity for the faculty to work on the development of skills, knowledge, activities and capabilities that used by the employees to ensure that the tasks assigned to them will be performed with skill, experience and passion, and at a high value.
Viewpoint Planning of Projector Placement for Spatial Augmented Reality using Star-Kernel Decomposition

To obtain the targeted projection appearance in a spatial augmented reality system, we need to solve the projector placement problem. However, there are only heuristic solutions for viewpoint planning, which is the fundamental problem of projector placement. In this paper, we propose the star-kernel decomposition algorithm that can solve the viewpoint planning problem of an object composed of known polygons. The star-kernel decomposition algorithm decomposes a polygon into subpolygons that have non-empty star kernels. We implemented a program to obtain the placement of multiple projectors and evaluated the applicability of the obtained placement.
The Applicability of Artificial Intelligence in Contractual Relationships

The appearance and the impacts of AI and digitalisation in the different types of legal work and in different legal areas and in relation to certain legal institutions, are examined and analysed nowadays by many researches, in many ways. In this study, we examine the impact digitalisation and AI have on the law of obligations, particularly on the law of contract and which challenges shall the national legislators face in the near future. In the first part of the study, we deal with the formation of contracts by electronic means. After the short review of the related Hungarian regulation in force, recent results of the EU legislation will be introduced, which was generated by both the expansion of digital content and digital services. In the second part of the study, attention will be paid to a relatively new phenomenon, the so-called smart contract. In the course of our examination, we attempt to designate the framework of the notion of smart contract and to draft all those questions relating to smart contracts, which shall be answered over time by the legislation and by the contract law regulation.
Robotics to Enhance the Teaching and Learning Process

In 21st-century learning academic, where collaboration, digital literacy, critical thinking, and problem-solving are considered the core competencies to be enhanced and developed further.In a multinational country, such as the United Arab Emirates (UAE) which remains one of the fastest-growing countries on the planet in all domains including education, tourism and health there is a gap in matching students with different background. To accommodate different background, heritage and education systems for the incoming expats and their family and kids, there are different education systems (America, British, Local, Indian to name but a few) that are currently running in the UAE. However, the move from one system to another is not a straightforward process due to many reasons which can be categorized into three groups: admission stage, leveling stage, and class stage. The implementation of the proposed work will be integrated as a case study in one of the British school systems in Abu Dhabi, UAE. This paper considers the application of the robotics in Education providing the background for current use of the robots in the society.
Concept Drift Detection in Dynamic Probabilistic Relational Models

Dynamic probabilistic relational models, which are factorized w.r.t. a full joint distribution, are used to cater for uncertainty and for relational and temporal aspects in real-world data. While these models assume the underlying temporal process to be stationary, real-world data often exhibits non-stationary behavior where the full joint distribution changes over time. We propose an approach to account for non-stationary processes w.r.t. to changing probability distributions over time, an effect known as concept drift. We use factorization and compact encoding of relations to efficiently detect drifts towards new probability distributions based on evidence.
F-SGX: Next Generation SGX for Trusted Computing

The existing methods of constructing a trusted computing environment do not fully meet the requirements. Intel SGX provides a new hardware foundation for the construction of trusted computing environment. However, existing SGX still faces problems such as side channel attacks. To overcome it, this paper present F-SGX which is the future SGX for trusting computing. In our opinion, F-SGX hold stronger isolation than current SGX, and reduce the dependence of enclave on host operating system. Furthermore, F-SGX hold a private key for the attestation. We believe that F-SGX can further provide better support for trusting computing environments while there is a good balance between isolation and dependencies.
When Design is Inspired by Theatre: Acting Techniques as Prospective Design Methods

In acting training, psychophysical exercises are used to strengthen the relationship between mind and body, thus fostering a deeper understanding of the character []. Intrigued and inspired by the potential value of these techniques in design contexts, we explored their application for interaction designers as research methods in a pedagogical setting. To do so, we first created a single-session workshop that introduced design students to basic actor movement techniques in the winter of 2019. The goal of the workshop was to help students empathize with their users and discover solutions when designing digital products. Later, in the fall of 2020, we used reflections from the first activity to develop two longer workshops; both consisted of three sessions and were carried out consecutively in two different universities. In this article, we present a case study of those three workshops. After discussing considerations for the evolution of the workshops, we describe how each was conducted. Finally, we share our findings and insights that arose throughout the process.
On Tibetan College Students’ Chinese Word Recognition Based on the High Speed Data Acquisition by SMI

Tibetan college students are typical trilinguists. Chinese, as their second language, is quite different from their mother tongue, Tibetan, in the rules of sound and spelling. So their Chinese word processing is quite different from monolinguists'. The article designed two experiments, including visual and auditory word recognition to check the processing order and processing load of sound, form and meaning in Chinese word recognition. Hi-speed of SMI was used to capture the data and SPSS was used to analyze the data. The experiments found that Tibetan college students' visual Chinese recognition was completed through the route of phonological mediation, while auditory Chinese recognition conformed to the theory of recombination of phonological representation The results showed that sound was crucial in the process of word recognition, so the teachers should take advantage of the feature to make their teaching more effective.