Reviewerly/exHarmony huggingface dataset:
Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model’s F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.
Does economic freedom affect entrepreneurship? Insights from Africa

Purpose - Literature suggests that entrepreneurship can serve as a veritable tool for providing decent employment and improving economic prosperity. Therefore, the objective of this study is to examine the impact of economic freedom on entrepreneurship in Africa. Design/methodology/approach - The study employs data of 18 African countries covering a period of 2007-2018. The analysis is based on the following techniques: Panel-Corrected Standard Errors (PCSE), generalized method of moments, Hausman–Taylor IV estimator and Driscoll-Kraay standard errors. Findings - Finding based on Panel-Corrected Standard Errors (PCSE) technique reveals that economic freedom and its dimensions improve the level of entrepreneurship in Africa. This finding is robust to other alternative estimation techniques. Secured property right, relaxed tax burden , monetary freedom, trade freedom, freedom from corruption, investment freedom, financial freedom, business freedom and labor freedom have positive impact on African entrepreneurship. Practical implications - The study, hence, suggests that policy should be implemented to maximize the level of economic and fundamental freedom of citizens to encourage indigenous entrepreneurs in Africa. Quality of infrastructure should be improved as well as simplification of firms’ registration procedures. African government also needs to build effective and efficient institutional framework to maintain government integrity in Africa. Originality/value - The position of African countries in the nexus between economic freedom and entrepreneurship is rarely discussed in the literature. Hence, this study contributes in this respect and showcases how economic freedom influence the decision to engage in entrepreneurial venture in African perspectives
Mask-guided contrastive attention and two-stream metric co-learning for person Re-identification

Person Re-identification (ReID) is an important yet challenging task in computer vision. Due to diverse background clutters, variations of viewpoints and body poses, it is far from being solved. How to extract discriminative and robust features invariant to background clutters is one of the core problems. In this paper, we first introduce a set of binary segmentation masks to construct synthetic RGB-Mask pairs as inputs, and then design a mask-guided contrastive attention model (MGCAM) to learn features separately from the body and background regions. Moreover, we propose a novel region-level triplet loss to guide the features learning, i.e., pulling the features from the full image and body region close, whereas pushing the features from backgrounds away. To learn the similarities from multiple features of the proposed MGCAM, we further introduce the instance-level two-stream metric co-learning (TSMCL) to help learn pair-wise relations between features from not only different regions but also different instances. TSMCL could help learn more compact features across full and body streams, enhancing the performance of MGCAM. We evaluate the proposed method on four public datasets, including MARS, Market-1501, CUHK03, and DukeMTMC-reID. Extensive experiments show that the proposed method is effective and achieves satisfying results.
Grey-Box Technique of Software Integration Testing Based on Message

Abstract In the software integration testing, the combined exploding problem was appeared for GUI (Graphic User Interface) software with many advantages. Based on the engineering thinking of staging and the feature of GUI software, the grey technique of integration testing for GUI Software based on message is proposed to solve this problem, in which, the white-box method is used to deal with the consistent and unified part and black-box method is mainly engaged to dispose the difficult and varied part of software testing. By analysis of basic GUI software, the application of this grey technique will improve the testing efficiency with 333% according to testing executed number, and the efficiency in terms of testing executed time will be 438%. An actual example in product quality monitoring software has verified the approach and its efficiency of the grey technique.
Data-driven insight into the puzzle-based cybersecurity training

Puzzle-based training is a common type of hands-on activity accompanying formal and informal cybersecurity education, much like programming or other IT skills. However, there is a lack of tools to help the educators with the post-training data analysis. Through a visualization design study, we designed the Training Analysis Tool that supports learning analysis of a single hands-on session. It allows an in-depth trainee comparison and enables the identification of flaws in puzzle assignments. We also performed a qualitative evaluation with cybersecurity experts and students. The participants apprised the positive influence of the tool on their workflows. Our insights and recommendations could aid the design of future tools supporting educators, even beyond cyber security.
Camera Selection for Occlusion-Less Surgery Recording via Training With an Egocentric Camera

Recording surgery is an important technique for education and the evaluation of medical treatments.However, capturing targets such as the surgical field, surgical tools, and the surgeon's hands, is almost impossible since these targets are heavily occluded by the surgeon's head and body during a surgery.We used a recording system in which multiple cameras are installed in the surgical lump, supposing at least one camera would capture the target without occlusion.As this system records multiple video sequences, we address the task to select a best view camera automatically.Recently, learning-based approaches in a fully supervised manner have been proposed for this task, but these previous approaches completely rely on manual annotation of the training data.In this paper, we focus on the eye tracker mounted on the surgeon's head, which can capture the recording targets without occlusion.Employing this first-person-view video synchronized with multiple videos of the surgical lump, we propose a novel camera selection approach using a self-supervised learning framework.In experiments, we created a dataset composed of four different breast surgery.Our extended experiments showed that our approach successfully switched to the best camera view without manual annotation and achieved competitive accuracy compared with conventional supervised methods.Also, our approach yielded effective visual representations comparable to state-of-the-art selfsupervised learning frameworks.
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling

Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding.However, it remains a major challenge for existing CSRL parser to handle conversational structural information.In this paper, we present a simple and effective architecture for CSRL which aims to address this problem.Our model is based on a conversational structure-aware graph network which explicitly encodes the speaker dependent information.We also propose a multi-task learning method to further improve the model.Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.
Perfectly Oblivious (Parallel) RAM Revisited, and Improved Constructions.

Oblivious RAM (ORAM) is a technique for compiling any RAM program to an oblivious counterpart, i.e., one whose access patterns do not leak information about the secret inputs. Similarly, Oblivious Parallel RAM (OPRAM) compiles a parallel RAM program to an oblivious counterpart. In this paper, we care about ORAM/OPRAM with perfect security, i.e., the access patterns must be identically distributed no matter what the program’s memory request sequence is. In the past, two types of perfect ORAMs/OPRAMs have been considered: constructions whose performance bounds hold in expectation (but may occasionally run more slowly); and constructions whose performance bounds hold deterministically (even though the algorithms themselves are randomized).In this paper, we revisit the performance metrics for perfect ORAM/OPRAM, and show novel constructions that achieve asymptotical improvements for all performance metrics. Our first result is a new perfectly secure OPRAM scheme with O(log³ N/log log N) expected overhead. In comparison, prior literature has been stuck at O(log³ N) for more than a decade. Next, we show how to construct a perfect ORAM with O(log³ N/log log N) deterministic simulation overhead. We further show how to make the scheme parallel, resulting in an perfect OPRAM with O(log⁴ N/log log N) deterministic simulation overhead. For perfect ORAMs/OPRAMs with deterministic performance bounds, our results achieve subexponential improvement over the state-of-the-art. Specifically, the best known prior scheme incurs more than √N deterministic simulation overhead (Raskin and Simkin, Asiacrypt'19); moreover, their scheme works only for the sequential setting and is not amenable to parallelization.Finally, we additionally consider perfect ORAMs/OPRAMs whose performance bounds hold with high probability. For this new performance metric, we show new constructions whose simulation overhead is upper bounded by O(log³ /log log N) except with negligible in N probability, i.e., we prove high-probability performance bounds that match the expected bounds mentioned earlier.
Media Literacy Education for Students During Learning Online the Covid-19 Pandemic

One of the impacts of the COVID-19 pandemic is felt in the world of education, namely the Government's policy that all learning activities must be carried out from home online. With this policy, students tend to be affected by negative impacts during online learning due to the increasing consumption of the digital world. Therefore, media literacy education is needed for students so that it can become the basic ability of students to overcome negative and negative influences to improve understanding to build knowledge and have more critical thinking about information obtained from the media, especially for students who are currently learning from home online. This study uses a research method with a literature review with data collection techniques carried out through an in-depth study of various reference sources. The results of the analysis show that media literacy education for students during online learning during the covid-19 pandemic is a basic skill that is so important for a student to have, not only for online learning but also for learning in the 21st century as it is today. Media literacy education skills can be applied during online learning by applying skills such as access, select, understand, analyze, verify, evaluate, distribute, produce, participate and collaborate
Core-Concept-Seeded LDA for Ontology Learning

Ontologies are powerful semantic models applied for various purposes such as improving system interoperability, information retrieval, question answering, etc. However, building domain ontologies remains a challenging task for humans, especially when the concepts and properties are large or evolving, and also when they are built from large-scale textual data. Machine learning allows to automate the building of ontologies from texts. In particular, clustering techniques have a promising ability on the concept formation task by identifying the cluster of semantically closed terms as a concept. However, current works encounter issues in learning relevant domain-specific clusters or in identifying the relevant concept labels for each cluster. To solve these issues, we propose both to use core concepts from a domain ontology as prior knowledge, and to adapt term clustering with seed knowledge-based LDA models in order to take these core concepts into account. First, each topic is associated with a set of seed terms of a single core concept, then the learning is guided by these seeds to gather in the same topic the terms that refer to its core concept. We evaluate our proposal on two textual corpora and compare it to the baselines (LDA, K-means, and SMBM). The results show that our approach performs significantly better than other methods on the class-balanced dataset and works well on the class-imbalanced dataset with a proper number of topics for each core concept.
Optimization of data allocation in hierarchical memory for blocked shortest paths algorithms

This paper is devoted to the reduction of data transfer between the main memory and direct mapped cache for blocked shortest paths algorithms (BSPA), which represent data by a D[M×M] matrix of blocks. For large graphs, the cache size S = δ×M2, δ &lt; 1 is smaller than the matrix size. The cache assigns a group of main memory blocks to a single cache block. BSPA performs multiple recalculations of a block over one or two other blocks and may access up to three blocks simultaneously. If the blocks are assigned to the same cache block, conflicts occur among the blocks, which imply active transfer of data between memory levels. The distribution of blocks on groups and the block conflict count strongly depends on the allocation and ordering of the matrix blocks in main memory. To solve the problem of optimal block allocation, the paper introduces a block conflict weighted graph and recognizes two cases of block mapping: non-conflict and minimum-conflict. In first case, it formulates an equitable color-class-size constrained coloring problem on the conflict graph and solves it by developing deterministic and random algorithms. In second case, the paper formulates a problem of weighted defective color-count constrained coloring of the conflict graph and solves it by developing a random algorithm. Experimental results show that the equitable random algorithm provides an upper bound of the cache size that is very close to the lower bound estimated over the size of a complete subgraph, and show that a non-conflict matrix allocation is possible at δ = 0.5 for M = 4 and at δ = 0.1 for M = 20. For a low cache size, the weighted defective algorithm gives the number of remaining conflicts that is up to 8.8 times less than the original BSPA gives. The proposed model and algorithms are applicable to set-associative cache as well.
Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction

Current methods for authentication and key agreement based on public-key cryptography are vulnerable to quantum computing. We propose a novel approach based on artificial intelligence research in which communicating parties are viewed as autonomous agents which interact repeatedly using their private decision models. Authentication and key agreement are decided based on the agents’ observed behaviors during the interaction. The security of this approach rests upon the difficulty of modeling the decisions of interacting agents from limited observations, a problem which we conjecture is also hard for quantum computing. We release PyAMI, a prototype authentication and key agreement system based on the proposed method. We empirically validate our method for authenticating legitimate users while detecting different types of adversarial attacks. Finally, we show how reinforcement learning techniques can be used to train server models which effectively probe a client’s decisions to achieve more sample-efficient authentication.
High performance computing for machine learning

Efficient High Performance Computing for Machine Learning has become a necessity in the past few years. Data is growing exponentially in domains like healthcare, government, economics and with the development of IoT, smartphones and gadgets. This big volume of data, needs a storage space which no traditional computing system can offer, and needs to be fed to Machine Learning algorithms so useful information can be extracted out of it. The larger the dataset that is fed to a Machine Learning algorithm the more precise the results will be, but also the time to compute those results will increase. Thus, the need for Efficient High Performance computing in the aid of faster and better Machine Learning algorithms. This paper aims to unveil how one benefits from another, what research has achieved so far and where is it heading.
Topic Detection Using Multiple Semantic Spider Hunting Algorithm

In every moment, there is a huge capacity of data and information communicated through social network. Analyzing huge amounts of text data is very tedious, time consuming, expensive and manual sorting leads to mistakes and inconsistency. Document dispensation phase is still not accomplished of extracting data as a human reader. Furthermore the significance of content in the text may also differ from one reader to another. The proposed Multiple Spider Hunting Algorithm has been used to diminish the time complexity in compare with single spider move with multiple spiders. The construction of spider is dynamic depends on the volume of a corpus. In some case tokens may related to more than one topic and there is a need to detect Topic on semantic way. Multiple Semantic Spider Hunting Algorithm is proposed based on the semantics among terms and association can be drawn between words using semantic lexicons. Topic or lists of opinions are generated from the knowledge graph. News articles are gathered from five dissimilar topics such as sports, business, education, tourism and media. Usefulness of the proposed algorithms have been calculated based on the factors precision, recall, f-measure, accuracy, true positive, false positive and topic detection percentage. Multiple Semantic Spider Hunting Algorithm produced good result. Topic detection percentage of Spider Hunting Algorithm has been compared to other algorithms Naïve bayes, Neural Network, Decision tree and Particle Swarm Optimization. Spider Hunting Algorithm produced more than 90% precise detection of topic and subtopic.
Website-Based Event Organizer Service Information System

Event Organizer is a service that functions to personally assist organizations or individuals in planning and supervising the implementation of a series of events according to a predetermined schedule and budget. Information System Event services created by utilizing web-based information technology, with the aim of making it easier for consumers to manage events and order packages online through a website that has been created. This event organizer service information system uses PHP and Sql programming languages, the method used in developing this system is using Prototyping which consists of analysis, design, application creation, evaluation and results.
Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points

In the present work, a neuronal dynamic response prediction system is shown to estimate the response of multiple systems remotely without sensors. For this, a set of Neural Networks and the response to the step of a stable system is used. Six basic characteristics of the dynamic response were extracted and used to calculate a Transfer Function equivalent to the dynamic model. A database with 1,500,000 data points was created to train the network system with the basic characteristics of the dynamic response and the Transfer Function that causes it. The contribution of this work lies in the use of Neural Network systems to estimate the behavior of any stable system, which has multiple advantages compared to typical linear regression techniques since, although the training process is offline, the estimation can perform in real time. The results show an average 2% MSE error for the set of networks. In addition, the system was tested with physical systems to observe the performance with practical examples, achieving a precise estimation of the output with an error of less than 1% for simulated systems and high performance in real signals with the typical noise associated due to the acquisition system.
Boosting Lightweight Single Image Super-resolution via Joint-distillation

The rising of deep learning has facilitated the development of single image super-resolution (SISR). However, the growing burdensome model complexity and memory occupation severely hinder its practical deployments on resource-limited devices. In this paper, we propose a novel joint-distillation (JDSR) framework to boost the representation of various off-the-shelf lightweight SR models. The framework includes two stages: the superior LR generation and the joint-distillation learning. The superior LR is obtained from the HR image itself. With less than $300$K parameters, the peer network using superior LR as input can achieve comparable SR performance with large models, e.g., RCAN, with 15M parameters, which enables it as the input of peer network to save the training expense. The joint-distillation learning consists of internal self-distillation and external mutual learning. The internal self-distillation aims to achieve model self-boosting by transferring the knowledge from the deeper SR output to the shallower one. Specifically, each intermediate SR output is supervised by the HR image and the soft label from subsequent deeper outputs. To shrink the capacity gap between shallow and deep layers, a soft label generator is designed in a progressive backward fusion way with meta-learning for adaptive weight fine-tuning. The external mutual learning focuses on obtaining interaction information from a peer network in the process. Moreover, a curriculum learning strategy and a performance gap threshold are introduced for balancing the convergence rate of the original SR model and its peer network. Comprehensive experiments on benchmark datasets demonstrate that our proposal improves the performance of recent lightweight SR models by a large margin, with the same model architecture and inference expense.
PhonHarp: A Hybrid Digital-Physical Musical Instrument for Mobile Phones Exploiting the Vocal Tract

This paper presents PhonHarp, a new interface for musical expression. The instrument can be described as a human-device loop, based on an Android app for mobile phones that produces sounds, which are modified by the vocal tract of the musician. After analyzing the state of the art, this work will provide details about functional requirements, design choices, and implementation aspects.