Reviewerly/exHarmony huggingface dataset:
Parametrizations and Basic Properties

A formal definition of an exponential family is the starting point of a widely self-contained, mathematically rigorous and concise course on exponential families in a systematic structure. The definition is followed by a variety of detailed examples of one- and multiparameter, uni- and multivariate probability distributions. Basic notions and representations of exponential families, such as minimal and canonical representation, natural parametrization, and mean value parametrization are introduced and structural results are presented, which are illustrated by examples.
FlashCube

Fast provisioning of serverless functions is salient for serverless platforms. Though lightweight sandboxes (e.g., containers) enclose only necessary files and libraries, a cold launch still requires up to a few seconds to complete. Such slow provisioning prolongs the response time of serverless functions and negatively impacts users' experiences. This paper analyzes the main reasons for such slowdown and introduces an effective containerization framework, FlashCube. Instead of building a container from scratch, FlashCube quickly and efficiently assembles it through a group of pre-created general container parts (e.g., namespaces, cgroups, and language runtimes). In addition, FlashCube's user-space implementation makes it easily applicable to existing commodity serverless platforms. Our preliminary evaluation demonstrates that FlashCube can quickly provision containerized functions in less than 10 ms (vs. ~400 ms using Docker containers).
Testing for Self-exciting Jumps in Bitcoin Returns

Cryptocurrencies, especially Bitcoin (BTC), have drawn extraordinary worldwide attention. The characteristics of BTC include a high level of speculation, extreme volatility, and price discontinuity. This paper investigates the self-exciting jumps in Bitcoin prices using a new nonparametric test proposed by Boswijk et al. (2018). We have the following discoveries. First, we document evidence of self-exciting jumps in Bitcoin returns, the strength of self-excitation does not increase monotonically with jump size, and the most self-excitation events occur with mild-sized jumps. Second, we find strong asymmetry in the self-excitation, which is triggered, on average, more by bad (negative) jumps than good (positive) jumps. Third, in bear markets, the self-excitation is triggered, on average, more by bad jumps than good jumps; in bull markets, however, the self-excitation triggered by good jumps is not more evident than bad jumps. Fourth, we find significant intraday aftershock effect of self-excitation, and the aftershocks triggered by bad jumps are more evident than the aftershocks triggered by good jumps. Finally, if we detect jumps by using a formal test, previous conclusions still hold and we document more evidence of self-exciting jumps.
Research on Operation Management and Maintenance Strategy of Communication Network

With the rapid development of computer network technology, communication network technology plays an important role in people’s life and work, seeking development space for all walks of life and winning huge profits. It not only makes the communication between people more convenient, but also brings a lot of convenience for people’s study and work. However, there are many problems in the development of communication network technology, which restricts its development. Aiming at these problems, this paper discusses the strategy of maintaining the operation of communication network, and traces its essence.
Low Noise Homomorphic Encryption Scheme Supporting Multi-Bit Encryption

Fully homomorphic encryption (FHE) provides effective security assurance for privacy computing in cloud environments. But the existing FHE schemes are generally faced with challenges including using single-bit encryption and large ciphertext noise, which greatly affects the encryption efficiency and practicability. In this paper, a low-noise FHE scheme supporting multi-bit encryption is proposed based on the HAO scheme. The new scheme redesigns the encryption method without changing the system parameters and expands the plaintext space to support the encryption of integer matrices. In the process of noise reduction, we introduce a PNR method and use the subGaussian distribution theory to analyze the ciphertext noise. The security and the efficiency analysis show that the improved scheme can resist the chosen plaintext attack and effectively reduce the noise expansion rate. Comparative experiments show that the scheme has high encryption efficiency and is suitable for the privacy-preserving computation of integer matrices.
Supplementing Omitted Named Entities in Cooking Procedural Text with Attached Images

In this research, we aim at supplementing named entities, such as food, omitted in the procedural text of recipe data. It helps users understand the recipe and is also necessary for the machine to understand the recipe data automatically. The contribution of this research is as follows. (1) We construct a dataset of Chinese recipes consisting of 12,548 recipes. To detect sentences in which food entities are omitted, we label named entities such as food, tool, and cooking actions in the procedural text by using the automatic recipe named entity recognition method. (2) We propose a method of recognizing food from the attached images. A procedural text of recipe data is often associated with an image, and the attached image often contains the food even when it is omitted in the procedural text. Tool entities in images in recipe data can be identified with high accuracy by conventional general object recognition techniques. On the other hand, the general object recognition methods in the literature, which assume that the properties of an object are constant, perform not well for food in recipe image data because food states change during cooking procedures. To solve this problem, we propose a method of obtaining food entity candidates from other steps that are similar to the target step, both in sentence similarity and image feature similarity. Among all the 246,195 procedural steps in our dataset, there are 16,593 steps in which the food entity is omitted in the procedural text. Our method is applied to supplement the food entities in these steps and achieves the accuracy of 67.55%.
A Framework for Statistical Nonlocal Means Noise Reduction in PolSAR Data

Fully Polarimetric SAR (Synthetic Aperture Radar), PolSAR, inherits the advantages of SAR systems (day and night imaging capacity, almost all-weather and active imaging), while extending its capabilities through the use of four polarization combinations (Horizontal and Vertical - HH, VV, HV, VH) [1]. PolSAR data integrates multiple types of rich information, such as frequency, amplitude and phase, which contributes to better characterizing different targets.
BETO Emotion Analysis of Facebook Users Reacting to Major Media Outlets in Costa Rica

An emotion analysis service, based on Transfer Learning, was built using BETO, and applied to Facebook posts written in Spanish. The system was tested using comments related to the anniversary of major media outlets in Costa Rica, the comments on these posts provide insight into the public opinion towards the mentioned media outlet. After collecting the comments and processing them into the emotion analyzer, the most common feelings for each media where: Amelia Rueda (joy), La Nación (anger and sadness) and Teletica.com (joy and anger).
Non-linear Relationship Learning Algorithm for Related Recommendations

In this paper, we introduce a novel non-linear relationship learning algorithm for related video recommendations on industrial video sharing platforms. As an indispensable part in recommender system, related video recommender system faces several real-world challenges, including maintaining high relevance between source item and target items, as well as achieving multiple competing ranking objectives. Typical solution is using latent factor CF models to calculate similarities between items. However, this approach usually uses the shallow projection, which cannot capture the characteristics of users and items well. To solve these, we introduce a Two-tower DNN structure to learn non-linear relationships from user-item interactions. Besides, we add a candidate generation stage for guaranteeing high correlation and a multi-task learning mechanism. Compared with baseline solutions, live experiments demonstrate that our proposed algorithm can significantly advance the state of the art on related recommendation quality.
Securing Mobile Cloud Computing Using Encrypted Biometric Authentication

Mobile cloud computing (MCC) is based on the integration of cloud computing and mobile devices that inherit cloud computing characteristics such as on-demand self-service, broad network access, and measured services. Also, MCC inherited the security threats of cloud computing, like loss of data, exposing the data to a third party, or unauthorized access to resources. Although there are many researches in the area of protecting data from illegitimate access using traditional encryption techniques, this paper discusses a new methodology for preventing unauthorized access to resources by encrypting user’s password along with its biometric identification (fingerprint) and storing them to the cloud. As a result, only authorized users can generate keys for encrypting their data and store them to the cloud. The proposed methodology will protect the identity of the user and keep user data from unauthorized access.
Progressive Multi-scale Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Gate Fusion Network

Depth maps obtained by consumer depth sensors are often accompanied by two challenging problems: low spatial resolution and insufficient quality, which greatly limit the potential applications of depth images. To overcome these shortcomings, some depth map super-resolution (DSR) methods tend to extrapolate a high-resolution depth map from a low-resolution depth map with the additional guidance of the corresponding high-resolution intensity image. However, these methods are still prone to texture copying and boundary discontinuities due to improper guidance. In this paper, we propose a deep residual gate fusion network (DRGFN) for guided depth map super-resolution with progressive multi-scale reconstruction. To alleviate the misalignment between color images and depth maps, DRGFN applies a color-guided gate fusion module to acquire content-adaptive attention for better fusing the color and depth features. To focus on restoring details such as boundaries, DRGFN applies a residual attention module to highlight the different importance of different channels. Furthermore, DRGFN applies a multi-scale fusion reconstruction module to make use of multi-scale information for better image reconstruction. Quantitative and qualitative experiments on several benchmarks fully show that DRGFN obtains the state-of-the-art performance for depth map super-resolution.
Enhanced A5 Algorithm for Secure Audio Encryption

This paper endeavours to bring out the fundamental mechanism of A5 encryption algorithm used in GSM cellular network, its lacunae and solutions to mitigate them. The A5 encryption algorithm is discussed with special emphasis on the strongest of all versions, the A5/1. The lacunae with respect to security of A5/1 algorithm are then brought out and methodology for mitigating them is discussed. A5/1 algorithm is modified and tested to confirm the lacunae and elucidation for their liquidation is proposed. The maximum period of existing A5/1 algorithm is 254 which is low and is susceptible to correlation attacks, whereas the maximum period of proposed model, i.e. Enhanced A5/1 is approximately 2128 on which cyberattacks are difficult to execute.
High-Level Synthesis of a Single/Multi-Band Optical and SAR Image Compression and Encryption Hardware Accelerator

Transmitting images from earth observation satellites to ground is a major challenge, and a compression/encryption stage is actually mandatory. Development of hardware accelerators is highly recommended, both to relieve the software from such demanding task, and to improve performance, aiming at quasi-real-time data processing. To this end, we discuss the design, development, deployment and test of a FPGA-based accelerator, featuring a lossless and lossy (near-lossless) compression, including the data encryption too. Its architecture is well suited for different image types, including single- and multi-band optical and SAR images and can be fully run-time configurable. Measured performance showed a throughput of 10 Msamples/s, in agreement with related state-of-the-art works, focused on lossless compression only.
Theoretical and methodological foundations of literary-artistic and linguistic education as a system

The article reveals the theoretical and methodological foundations of literary-artistic and linguistic education as a system. Several definitions of key concepts are analyzed: "system", "literary-artistic education", "language education". The system status of literary-artistic and linguistic education required highlighting its component element: teleological (ELA ideal/goal, ELA goal system, ELA standards), technological (operational block – learning activities and opportunities) and epistemological (theories, concepts, principles, ideas, precepts).
An Optimization Approach to Multi-Sensor Operation for Multi-Context Recognition

Mobile devices and sensors have limited battery lifespans, limiting their feasibility for context recognition applications. As a result, there is a need to provide mechanisms for energy-efficient operation of sensors in settings where multiple contexts are monitored simultaneously. Past methods for efficient sensing operation have been hierarchical by first selecting the sensors with the least energy consumption, and then devising individual sensing schedules that trade-off energy and delays. The main limitation of the hierarchical approach is that it does not consider the combined impact of sensor scheduling and sensor selection. We aimed at addressing this limitation by considering the problem holistically and devising an optimization formulation that can simultaneously select the group of sensors while also considering the impact of their triggering schedule. The optimization solution is framed as a Viterbi algorithm that includes mathematical representations for multi-sensor reward functions and modeling of user behavior. Experiment results showed an average improvement of 31% compared to a hierarchical approach.
Exploring the Spatial-Visual Locality of Geo-tagged Urban Street Images

Urban street images have a unique property as they capture visual scenes that are distinctive to their geo-graphical regions. Such images are similar to their neighboring ones while dissimilar to faraway images. We refer to this characteristic of images as the spatial visual locality or the spatial locality of similar visual features. This study focuses on geo-tagged urban street images and hypothesizes that those images demonstrate a local similarity in a certain region but a dissimilarity across different regions, and provides different analysis methods to validate the hypothesis. The paper also evaluates the correctness of the hypothesis using three real geo-tagged street images collected from the Google Street View. Our experimental results demonstrate a high locality of similar visual features among urban street images.
Real-time Virtual Simulation Platform for Multi-UVA hunting target using Deep Reinforcement Learning

As being one of the most useful flying objects, quadrotor UAVs are gradually being applied to the military field in the form of "swarms". However, an urgent problem is how to improve the intelligence of UAV with low cost. In this paper, we propose a hunting scenario for multi-UAV in an urban environment. Firstly, We build the game model of multi-UAV hunting target and design the reward function. Secondly, we introduce an improved multi-agent deep deterministic policy gradient (MADDPG) to against an game AI target. Then, we build a virtual platform to simulate the urban combat environment based on the Unity3D game engine. The ML-Agents Toolkit is used to develop a real-time simulation data interface to achieve low-cost model training. Finally, simulation results from Python and virtual environment can demonstrate the effectiveness of the proposed hunting scenario.
Investigating Player Experience in Virtual Reality Games via Remote Experimentation

This research explores player experience of virtual reality (VR) games through two stages of study. In both stages, we employed the Player Experience Inventory (PXI), a validated tool designed to evaluate player experience. In Stage 1, player experience of VR games was investigated via an online survey with 100 participants. We found that Audio-Visual Appeal, Immersion, and Ease of Control contributed most to player experience in VR games. We found no relationship between player experience and age, time spent playing, VR experience, or VR headset. Stage 2 used remote experimentation to compare VR and non-VR games with 10 participants. We found that differences in player experience can be explained by the Immersion, Progress Feedback, and Curiosity constructs of the PXI.
Need analysis “a first step” of ESP Material design” for Tourism vocational high school EFL Students

This paper aims at developing Speaking Teaching materials for tourism Vocational High school students at Toraja _Indonesia. Since this paper concern on material developing for tourism vocational school, hence this research focus on ESP Topic. To Develop teaching material, the researcher implemented 4D model Proposed by Tiagajaran, which consist of 4 Phase of the development stage, Define, Design, Develop, and Disseminate. In this paper, the writer focuses only on the first four phases. The define phase is the first step to track students' needs related to the topic or material being developed, therefore Need analysis is the pivotal aspect to be administered. Of the two main three main aspects in need analysis (Target need, Students need, and Learning need) the researcher limited this study only target need. In this research, 27 students in the age of 15 to 16 years who registered at hospitality vocational high Scholl were involved and chosen by using total sampling. In this study, only one instrument was used to find out the student target need, which is a questionnaire. The results of tourism vocational high school students’ necessity, lack, and wants were elaborated in this paperKeywords- Material design, 4D, need analysis, Target need, vocational high school
Comparative Analysis of Multi-Verse Optimizer with Time Freeze Effect and basic Multi-Verse Optimizer

In this paper an improvement of a well-known nature inspired meta-heuristic algorithm is compared with the original algorithm i.e. Multi-Verse Optimizer (MVO). Multi-Verse Optimizer with Time Freeze Effect (MVOTFE) is compared with original MVO in terms of optimization performance, time taken and convergence behavior. MVOTFE is first tested with same 19 benchmark test problems that are used by MVO. MVOTFE uses same mathematical model as used by MVO with only addition of time freeze effect in the algorithm. Comparative analysis suggests that MVOTFE not only outperforms original MVO in 16 out of 19 test problems but also provides faster convergence in lesser time. As indicated by convergence curves, it also effectively escapes from local optima present in the search space.
Constructing Sentiment Lexicon with Game for Annotation Collection

While research of sentiment analysis became very popular on the global scope, in Slovak language as an under-resourced language there are still many issues to be tackled, especially the lack of resources. In this paper, we introduce a sentiment analysis game designed to collect sentiment annotations. The game is intended for a single player who, motivated by game score, chooses the sentiment category of each word of a sentence. We describe the annotation collection process during which over 12 500 annotations of individual words and over 1 000 annotations of entire sentences were obtained within a week. The collected annotations were used to construct a sentiment lexicon. Using artificial bee colony algorithm, optimal lexicon construction parameters were discovered. To evaluate the final lexicon's usefulness, we applied simple sentence-level lexicon-based sentiment analysis methods on a manually annotated dataset of mobile phone reviews. The same was done with other existing lexicons for comparison. The results of our experiments show that collecting annotations using our game can be useful as a method for constructing a sentiment lexicon.
Continual Learning Using Lattice-Free MMI for Speech Recognition

Continual learning (CL), or domain expansion, recently became a popular topic for automatic speech recognition (ASR) acoustic modeling because practical systems have to be updated frequently in order to work robustly on types of speech not observed during initial training. While sequential adaptation allows tuning a system to a new domain, it may result in performance degradation on the old domains due to catastrophic forgetting. In this work we explore regularization-based CL for neural network acoustic models trained with the lattice-free maximum mutual information (LF-MMI) criterion. We simulate domain expansion by incrementally adapting the acoustic model on different public datasets that include several accents and speaking styles. We investigate two well-known CL techniques, elastic weight consolidation (EWC) and learning without forgetting (LWF), which aim to reduce forgetting by preserving model weights or network outputs. We additionally introduce a sequence-level LWF regularization, which exploits posteriors from the denominator graph of LF-MMI to further reduce forgetting. Empirical results show that the proposed sequence-level LWF can improve the best average word error rate across all domains by up to 9.4% relative compared with using regular LWF.
Problems of Professional L2 Discourse Formation for the Second-Year Technical Students in the Context of e-Learning Environment

The research focuses on the results of the survey conducted in Moscow State University of Food Production (MSUFP) to define the problems connected with professional L2 Discourse formation for the second-year technical students in the context of e-learning environment. The goal of the research work is to determine and analyze factors the second-year students consider to be most important to interfere their capability to use L2 receptive and productive skills for a professional discourse development. The theme of the research demonstrates the ongoing relevance and value according to a steady tendency of wide implementation of e-learning teaching forced by the dramatic changes connected with Covid-19 pandemic. Nowadays students consider e-learning environment to be an integral part of the educational field. The necessity and timeliness of such a research work is based on the fact that modern higher education implements various types of educational models, and includes more and more e-learning courses replacing traditional face-to face tutorials. This, in turn, requires to put close attention to possible transformation of the problems traditional for offline educational processes. The research was carried out through online interviewing of a group of 47 second-year students learning English as a second language as a part of their professional curriculum. The practical use of the results can be taken into account while arranging e-learning L2 professional acquisition for technical students to help them form L2 professional discourse.