Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF USER EXPERIENCE RESOURCE PLANNING WITH USER EXPERIENCE QUESTIONNAIRE FRAMEWORK (CASE STUDY: UNIVERSITAS MULTIMEDIA NUSANTARA)

ERP or Enterprise Resource Planning is a design that has been applied in an educational institution or company that can be used to organize, coordinate and manage the system resources in it to be more integrated. The refusal of system users to use the ERP system (new system) is one of the failure factors for ERP system implementation that should be considered by higher education companies. User's lack of understanding by users can cause users to simply be forced to use without being adapted to a qualified use of the ERP system. In addition to the importance of the ERP implementation process in a company or university, there is also an influence on the system's user experience as the key to the success of ERP implementation to minimize failures in the ERP system process. &#x0D; This study uses the User Experience Questionnaire Framework to analyze, measure and find out significantly how much influence the user experience level has on the PeopleSoft Oracle Campus Solution ERP system using the six aspects contained in the framework UEQ on the modules Student Record, Academic Advisement, and Student Financial in the respective departments at UMN. The research was conducted quantitatively, namely the collection and collection of data to be carried out through the distribution of questionnaires online and carried out using SPSS software. &#x0D; The results of the measurement level obtained based on the mean and benchmark data user experience from the six UEQ scales from the highest to the lowest value level are Dependability is in the category (Excellent), Stimulation is in the category (Excellent), Novelty is in the category (Excellent), Perspicuity category is in the category (Good), Attractiveness is in the category (Good), and Efficiency is in the category (Good) and partially or completely can have a significant 61% influence on the use of modules in the ERP system at UMN. &#x0D; &#x0D; Keywords: ERP (Enterprise Resource Planning), UEQ (User Experience Questionnaire), Quantitative, SPSS.
Gradient-Constraint Super-Resolution Reconstruction Method Serving for Infrared Target Detection

Infrared images generally suffer from insufficient resolution, blurred edge, and low contrast, which increases the difficulty of infrared target detection. To solve this problem, a multiframe image super-resolution (SR) algorithm based on edge gradient regularization is proposed. In order to improve the resolution of the infrared image while simultaneously improving the visual saliency of weak infrared target, an edge-preserving regularization term is designed and introduced into the solving process of Bayesian problem. On this basis, an image enhancement method based on gradient-constraint decomposition is proposed to improve the contrast of the infrared image, which further lays a foundation for infrared target detection. To verify the effectiveness of the algorithm, experiments are respectively carried out on FLIR infrared datasets, real infrared images captured statically and real infrared images captured by a UAV equipped with an infrared camera. The experimental results demonstrate that the proposed method is capable of generating high-resolution images with good performance in terms of edge preservation and detail enhancement. Compared with the original input infrared images, the edge saliency of the targets inside the SR reconstructed infrared images is significantly enhanced, and thus provides a very promising image preprocessing method for infrared target detection.
Proactive Scheduling and Caching for Wireless VR Viewport Streaming

Virtual Reality (VR) applications require high data rate for a high-quality immersive experience, in addition to low latency to avoid dizziness and motion sickness. One of the key wireless VR challenges is providing seamless connectivity and meeting the stringent latency and bandwidth requirements. This work proposes a proactive wireless VR system that utilizes information about the user’s future orientation for proactive scheduling and caching. This is achieved by leveraging deep neural networks to predict users’ orientation trained on a real dataset. The 360° scene is then partitioned using an overlapping viewports scheme so that only portions of the scene covered by the users’ perceptive field-of-view are streamed. Furthermore, to minimize the backhaul latency, popular viewports are cached at the edge cloud based on spatial popularity profiles. Through extensive simulations, we show that the proposed system provides significant latency and throughput performance improvement, especially in fluctuating channels and heavy load conditions. The proactive scheduling enabled by the combination of machine learning prediction and the proposed viewport scheme reduces the mean latency by more than 80% while achieving successful delivery rate close to 100%.
Multi-document detection via corner localization and association

With the development of hand-held photographic devices, document images in unconstrained environments can be captured in high-speed and high-resolution. It will be more efficient to process the text information of multiple documents simultaneously. In this paper, we propose a multi-document detection approach. It can estimate the amount of documents and also detect their accurate locations via iteratively searching the four corners and their direction maps from individual document in the image. Even for slightly occluded documents, the proposed method can infer the hidden corner positions. The model is designed to jointly learn the corner categories, locations and their directions in attentional regions via two branches of the same sequential prediction process. The association score is calculated based on the them between two corner connections. The graph theory, considering corners as nodes and association scores as edges, is applied to get the quadrangle for each document in image. For evaluation, we collect a Multi-Doc data set which contains 2,200 document images in various natural scenes. We show that the baseline model trained on this collected data and bench-mark SmartDoc 2015 can detect both single and multiple documents accurately and effectively.
FLAR: A Unified Prototype Framework for Few-sample Lifelong Active Recognition

Intelligent agents with visual sensors are allowed to actively explore their observations for better recognition performance. This task is referred to as Active Recognition (AR). Currently, most methods toward AR are implemented under a fixed-category setting, which constrains their applicability in realistic scenarios that need to incrementally learn new classes without retraining from scratch. Further, collecting massive data for novel categories is expensive. To address this demand, in this paper, we propose a unified framework towards Few-sample Lifelong Active Recognition (FLAR), which aims at performing active recognition on progressively arising novel categories that only have few training samples. Three difficulties emerge with FLAR: the lifelong recognition policy learning, the knowledge preservation of old categories, and the lack of training samples. To this end, our approach integrates prototypes, a robust representation for limited training samples, into a reinforcement learning solution, which motivates the agent to move towards views resulting in more discriminative features. Catastrophic forgetting during lifelong learning is then alleviated with knowledge distillation. Extensive experiments across two datasets, respectively for object and scene recognition, demonstrate that even without large training samples, the proposed approach could learn to actively recognize novel categories in a class-incremental behavior.
Regularized Forward-Backward Decoder for Attention Models

Nowadays, attention models are one of the popular candidates for speech recognition. So far, many studies mainly focus on the encoder structure or the attention module to enhance the performance of these models. However, mostly ignore the decoder. In this paper, we propose a novel regularization technique incorporating a second decoder during the training phase. This decoder is optimized on time-reversed target labels beforehand and supports the standard decoder during training by adding knowledge from future context. Since it is only added during training, we are not changing the basic structure of the network or adding complexity during decoding. We evaluate our approach on the smaller TEDLIUMv2 and the larger LibriSpeech dataset, achieving consistent improvements on both of them.
FMSM: A Fuzzy Multi-keyword Search Scheme for Encrypted Cloud Data based on Multi-chain Network

Searchable encryption is an efficient and secure solution that can perform search operation in ciphertext on cloud servers. It is widely used by a growing number of businesses and individuals. However, most existing searchable encryption schemes typically overestimate the security level of the cloud server, which is an untrusted and centralized third party in fact. The emergence of blockchain technology solves the trustworthiness problem of the third party with features such as decentralization, verifiability and immutability. The combination of searchable encryption and blockchain technology uses the smart contract instead of the cloud server to execute ciphertext search task, which improves security and efficiency of search significantly. We propose a Fuzzy Multi-keyword Search Scheme for Encrypted Cloud Data based on Multi-chain Network (FMSM). FMSM is a distributed searchable encryption scheme since we adopt a multi-chain architecture. It not only supports fuzzy multi-keyword search and dynamic updates for encrypted cloud data, but also supports parallel processing of data. In FMSM, counting bloom filter and MinHash algorithm are used to construct index vectors and query vectors on the corresponding two chains, besides, the search is done on a third chain. All of these calculation tasks are executed in the way of distributed computing, which makes the index construction and search process more efficient and the sorting results more accurate. The security analysis and performance evaluation show that the FMSM has high security, reliability, search efficiency and accuracy.
Potential Fields Guided Deep Reinforcement Learning for Optimal Path Planning in a Warehouse

Using mobile robots for transportation in a warehouse is becoming more and more common. Compared with human staff, these robots can handle the goods more accurately and more efficiently. Using robots can greatly reduce the operation cost of a warehouse. Optimal path planning for these robots can reduce the transportation time, guarantee the safety of the people in the warehouse, and reduce the goods delivery time and increase daily output. In this paper, we propose an optimal path planning algorithm for the mobile robot using deep reinforcement learning (DRL). Potential fields are employed to guide to collect better quality training data to improve data efficiency. The simulation results have shown that DRL can successfully reach the goal position and avoid collision with the obstacles using the potential fields guided trail-and-error method.
Interhead Hydra: Two Heads are Better than One

Distributed ledger are maintained through consensus protocols which have inherent limitations to their scalability. Layer-2 protocols operate on channels and allow parties to interact with another without going through the consensus protocol albeit relying on its security as fall-back. Channels can be concatenated into networks using techniques such as Hash Timelock Contracts (HTLC) to execute payments or virtual state channels as introduced by Dziembowski et al. [CCS’18] to execute state machines across a path of channels. This is realized by utilizing intermediaries, which are the parties on the channel path between both endpoints, who have to pay collateral to ensure security of the constructions. Dziembowski et al. [Eurocrypt’19] introduced multi-party state channels based on a virtual channel construction and more recently Hydra heads [FC’21] is a channel construction that allows multiple parties the execution of Constraint Emitting Machines (CEM). While existing protocols such as HTLCs can be extended such that two parties can interact with another across channels, there are no dedicated constructions that utilize multi-party channels and similarly allow more than two parties to interact across a network of channels. This work addresses this gap by extending Hydra and introducing the Interhead construction that allows for the iterative creation of virtual Hydra heads. Our construction is the first that (1) supports and utilizes multi-party channels and (2) allows for collateral to be paid by multiple intermediaries.
Precursor

As offered by the Intel Software Guard Extensions (SGX), trusted execution enables confidentiality and integrity for off-site deployed services. Thereby, securing key-value stores has received particular attention, as they are a building block for many complex applications to speed-up request processing. Initially, the developers' main design challenge has been to address the performance barriers of SGX. Besides, we identified the integration of a SGX-secured key-value store with recent network technologies, especially RDMA, as an essential emerging requirement. RDMA allows fast direct access to remote memory at high bandwidth. As SGX-protected memory cannot be directly accessed over the network, a fast exchange between the main and trusted memory must be enabled. More importantly, SGX-protected services can be expected to be CPU-bound as a result of the vast number of cryptographic operations required to transfer and store data securely.
Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction

Most existing human matting algorithms tried to separate pure human-only foreground from the background. In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image. The VMFM method requires no additional inputs, e.g. trimap or known background. We reformulate foreground matting as a self-supervised multi-modality problem: factor each input image into estimated depth map, segmentation mask, and interaction heatmap using three auto-encoders. In order to fully utilize the characteristics of each modality, we first train a dual encoder-to-decoder network to estimate the same alpha matte. Then we introduce a self-supervised method: Complementary Learning(CL) to predict deviation probability map and exchange reliable gradients across modalities without label. We conducted extensive experiments to analyze the effectiveness of each modality and the significance of different components in complementary learning. We demonstrate that our model outperforms the state-of-the-art methods.
The Bures Metric for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are performant generative methods yielding high-quality samples. However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping. To address this problem, we use the last layer of the discriminator as a feature map to study the distribution of the real and the fake data. During training, we propose to match the real batch diversity to the fake batch diversity by using the Bures distance between covariance matrices in this feature space. The computation of the Bures distance can be conveniently done in either feature space or kernel space in terms of the covariance and kernel matrix respectively. We observe that diversity matching reduces mode collapse substantially and has a positive effect on sample quality. On the practical side, a very simple training procedure is proposed and assessed on several data sets.
ZXAD

The Tor anonymity network is often abused by attackers to (anonymously) convey attack traffic. These attacks abuse Tor exit relays (i.e., the relays through which traffic exits Tor) by making it appear the attack originates there; as a result, many website operators indiscriminately block all Tor traffic (by blacklisting all exit IPs), reducing the usefulness of Tor.
Impact of Doppelgängers on Face Recognition: Database and Evaluation

Lookalikes, a.k.a. doppelgängers, increase the probability of false matches in a facial recognition system, in contrast to random face image pairs selected for non-mated comparison trials. In order to analyse and improve the robustness of automated face recognition, datasets of doppelganger face image pairs are needed. In this work, we present a new face database consisting of 400 pairs of doppelgänger images. Subsequently, two state-of-the-art face recognition systems are evaluated on said database and other public datasets, including the Disguised Faces in The Wild (DFW) database. It is found that the collected image pairs yield very high similarity scores resulting in a significant increase of false match rates. To facilitate reproducible research and future experiments in this field, the dataset is made available.
Little Computer People

Bolstered by a growing interest in simulating believable non-player characters (NPCs), work on NPC models has spanned topics such as planning, procedural storytelling, decision-making, and social dynamics. However, research groups work in isolation, designing and discussing their character models with disparate approaches, often using project-specific terminology. This makes it challenging to identify, classify, and accumulate existing knowledge. It is our position that since modelling of virtual characters has become an integral part of the scientific practice in our field, we must develop a common taxonomy to discuss these models. With this goal in mind, we conduct an in-depth analysis of a selection of projects, categorizing existing agent social interactions, and comparing results from research-based and commercial social simulation works in the entertainment domain. We conceptualize a taxonomy that classifies agent interactions by their social behaviours, inter-agent communication, knowledge flow, and the change in their relationships. We posit such a taxonomy would allow scientists to reproduce and evaluate existing models, collaborate on standards, share advances with other researchers and practitioners, allow for better communication and methodologies developed for new techniques, and allow for a more rigorous model-to-model analysis.
Using nested mappings for heat capacity data processing

In the article discrete approach based on self-similar hierarchical system of recurrent equations is proposed. The basic element of this system is linear stationary recurrent equation with constant coefficients. In general, such system has an endless nested structure, limited by the level, where its coefficients become constants. For processing heat capacity data, the proposed system was reduced to equation with uniformly varying coefficients, which is able to successfully describe a wide range of dependencies and make reliable predictions. The flexibility and convenience of this approach were shown by the example of various descriptions of heat capacity data of materials in different states and temperature ranges.
Research and Practice of China's Intelligent Coal Mines

This paper reviewed the development process and the current status of comprehensive mechanized coal mining equipment technology in China. The definition and technical connotation of the intelligent coal mine on the basis of the artificial intelligence and technology of the Internet of Things (IoT) are proposed. The paper researched and practiced the key technology related to the high efficient and adaptive shearer autonomous positioning technology, shearer autonomous obstacle avoidance technology, intelligent diagnosis of coal mining equipment, intelligent recognition technology of coal-rock interface. Through theoretical research and application practice, the feasibility, necessity, and advancement of the intelligent coal mine are proved. Finally, this paper looks forward to the development direction of intelligent coal mines and puts forward the development concept of coal-based multielement clean energy collaborative mining and the development and utilization integration of coal
CNN vs. LSTM for Turkish Text Classification

In this paper, the efficiency of two states of the art text classification techniques, i.e., Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for supporting the Turkish text classification has been investigated. In addition, the effect of the main preprocessing steps such as Tokenization, Stop Word Elimination, Stemming, etc. has also been studied. Several experiments using "TTC-3600" dataset were performed, and it has been observed that both CNN and LSTM can efficiently support the Turkish language and can achieve quite good performance. Related to data preprocessing, results indicated that such a process improves the performance, however, for the Turkish language, it is preferred to exclude stemming. Also, by comparing the performance of feature extraction techniques for processing Turkish language, Word2Vec outperforms TF-IDF.
IoT Devices Authentication Using Artificial Neural Network

User authentication is one of the critical concerns of information security.Users tend to use strong textual passwords, but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile phones.Textual passwords are slightly unprotected and are easily attackable.The attacks include dictionary, shoulder surfing, and brute force.Graphical passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of use.This paper proposes a Process-based Pattern Authentication (PPA) system for Internet of Things (IoT) devices that does not require a server to maintain a static password of the login user.The server stores user's information, which they provide at the time of registration, i.e., the R-code and the symbol, but the P-code, i.e., the actual password, will change with every login attempt of users.In this scheme, users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern, and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network (ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing methods.For experimental purposes, PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and usability.The experiments show that the system has an effect of 5.03% of the False Rejection Rate (FRR) and 4.36% of the False Acceptance Rate (FAR), respectively.