Reviewerly/exHarmony huggingface dataset:
An efficient object detection framework with modified dense connections for small objects optimizations

Object detection frameworks for small objects are increasingly demanded in some specific fields such as high-speed object tracking and remote sensing image recognition. In this paper, we propose an efficient object detection framework with modified dense connections for small objects. In order to improve both the detection accuracy and speed for small objects, the proposed framework constructs a convolutional neural network by using modified dense and residual cross-layer connections between multi-scale convolutional layers to extract deep features effectively. Based on the modified dense structure, a hybrid-scale feature fusion method is proposed to concatenate the multi-channel high-dimensional features and performs cross-entropy calculation and regression prediction. By using this method, this framework not only improves the detection accuracy for small objects significantly, but also improves the overall detection accuracy and optimizes the network parameters to reduce the detection time greatly. The experimental results show that the proposed framework achieves 90.6% mAP for small objects on a public ship dataset which is 25.2% more than SSD-VGGNet. Due to the detection efficiency for small objects, it improves the overall detection accuracy and detection speed by 9% and 40% respectively while about 70% network parameters are reduced.
Authentication Database Leakage Detection

Abstract Authentication is the first step for any important applications. Password verification is widely employed in the Internet. Some advanced mechanisms including bio features, such as fingerprints or retina. However, digital password authentication credential is commonly used in the public. In other words, traditional password verification is still an important authentication credential mechanism for today’s online services. Password database may be compromised. Once if the database is stolen, it is dangerous and critical for any services. Consequently, password database leakage becomes an important issue. Detection and countermeasure are essential for such a disaster. Our research proposing a scheme based on a authentication database storage mapping method to detect possible leakage of authentication database. By multiple mapping mechanism, if the authentication database is stolen, an attacker could not know any user’s correct password. This method can reduce additional space of storing passwords with ability to detect security event for stolen authentication databases.
Algorithms for Constructing Anonymizing Arrays

Attribute-based methods are inherently identity-less as authorization decisions are made in terms of attributes possessed by the subject rather than identity. However, anonymity against the system is not guaranteed when attribute distribution allows for the composition of a policy that few subjects can satisfy. An anonymizing array ensures that any assignment of values to t attributes that appears in the array appears at least r times. When an anonymizing array is used for subjects registered to a system and policies contain conjunctions of at most t attributes, the system cannot identify the subject using the policy to to gain authorization with greater than $$\frac{1}{r}$$ probability. Anonymizing arrays are similar to covering arrays with higher coverage and constraints, but have an additional desired property, homogeneity, due to their application domain. In this paper, we develop constructions for anonymizing arrays and propose a post-optimization mechanism to reduce homogeneity.
Design of an anti-interference lightning current acquisition module

To improve the anti-interference performance of a lightning current acquisition device, a redundant signal conditioning channel is adopted, i.e., an additional signal acquisition channel is provided to acquire the lightning current signal, and a dual-channel ADC is applied to acquire the two lightning current signals induced by the same conductor simultaneously. Then, the acquired data are sent to a DSP for similarity matching. If the similarity reaches the set value, the two lightning current data are considered to be matched and the lightning current data are determined to be valid and then stored, otherwise discarded, waiting for the arrival of next data. In this way, the interference signals are eliminated with true lightning current signals being retained only while the lightning current characteristic parameters are acquired and the lightning current signal waveform is reproduced, thereby improving the accuracy of lightning current monitoring. Also, this saves valuable storage resources, improves the processing efficiency and ensures real-time monitoring.
Web Service Active Defense Mechanism Based on Automated Software Diversity

Aiming at the problems of limited number and diversity in existing web server software, an active defense mechanism for web services based on automated software diversity is proposed, and the system framework and implementation are introduced. The formula proves that the probability of an attack escape event is low. Security and performance experiments show that the active defense mechanism of web services based on automated software diversity can resist or detect memory error attacks with less overhead, indicating that automated software diversity can effectively improve the security of web services. This verifies its effectiveness and feasibility. Finally, the research prospects and improvement directions are given according to the implementation granularity of automated software diversity and the monitoring granularity of web services.
Evaluating the Transferability of Personalised Exercise Recognition Models

Exercise Recognition (ExR) is relevant in many high impact domains, from healthcare to recreational activities to sports sciences. Like Human Activity Recognition (HAR), ExR faces many challenges when deployed in the real-world. For instance, typical lab performances of Machine Learning (ML) models, are hard to replicate, due to differences in personal nuances, traits and ambulatory rhythms. Thus effective transferability of a trained ExR model, depends on its ability to adapt and personalise to a new user or a user group. This calls for new experimental design strategies that are person-aware, and able to organise train and test data differently from standard ML practice. Specifically, we look at person-agnostic and person-aware methods of train-test data creation, and compare them to identify best practices on a comparative study of personalised ExR model transfer. Our findings show that ExR when compared to results with other HAR tasks, to be a far more challenging personalisation problem and also confirms the utility of metric learning algorithms for personalised model transfer.
Interactive proof-search for equational reasoning

Abstract Equational reasoning arises in many areas of mathematics and computer science. It is a cornerstone of algebraic reasoning and results essential in tasks of specification and verification in functional programming, where a program is mainly a set of equations. The usual manipulation of identities while conducting informal proofs obviates many intermediate steps that are neccesary while developing them using a formal system, such as the equationally complete Birkhoff calculus ${\mathcal{B}}$. This deductive system does not fit in the common manner of doing mathematical proofs, and it is not compatible with the mechanisms of proof assistants. The aim of this work is to provide a deductive system ${\mathcal{B}}^{\textrm{GOAL}}$ for equality, equivalent to ${\mathcal{B}}$ but suitable for constructing equational proofs in a backward fashion. This feature makes it adequate for interactive proof-search in the approach of proof assistants. This will be achieved by turning ${\mathcal{B}}^{\textrm{GOAL}}$ into a transition system of formal tactics in the style of Edinburgh LCF, such transformation allows us to give a direct formal definition of backward proof in equational logic.
The Design of Virtual Reality Whole Body Vibration Device and Contents for Improving Cognitive Ability of The elderly

In recent years, the aging society has been rapidly progressing as the development of medical technology and the awareness of healthy life have become more important. However, the elderly tend to be passive in their physical and social activities. Considering these environmental factors, proper exercise equipment should be used for them. In case of the whole body vibrator, positive effects for strengthening of the muscular power of the elderly, postural balance, and gait function were verified. For the elderly athletic equipment, considerations are as follows. First, it should be more effective than other programs inducing interest, an active participation and motivating users. Second, there should be no inconvenience of movement and it should be more secure than anything else. In case of exercise program using virtual reality, it is possible to apply various kind of exercise easily and comfortably. However, there are not many virtual reality exercise equipments available that the elderly need. In particular, the research and development of virtual reality whole body vibration devices is underway. In this paper, I propose a whole body vibration device that can interlock with virtual reality contents using whole body vibration device based on existing research. Also, intelligent level system is applied to have positive effect as an exercise. The intelligent level system considerations include deriving the appropriate level function for the game level. That is, it is an application of an efficient automatic level determination system that can automatically determine the level suitable for the exercise ability. First, I propose virtual reality contents suitable for whole body vibration device. Second, the interface is designed to support various senses. Third, it is designed to obtain satisfaction and an achievement as a compensation factor for exercise.
Intra-Attention and Inter-Attention for Aspect-level Sentiment Classification

Aspect-level sentiment classification is an important tool for e-business platforms to help the user and suppliers to summary the fine-grained aspect-based sentiment opinions. The same review document may have different sentiment polarity for each aspect. Existing aspect-level sentiment classification usually learn the aspect-based sentiment polarity with traditional supervised learning and deep learning models with inter-attention, which may be insufficient. In this paper, we propose an intra- and inter-attention network model to predict the aspect-level of sentiment category. The core of our model is the representation module, intra-attention module and inter-attention module. In the representation module, we use the character-level semantic and word-level semantic information, respectively. In the intra-attention module, we use the intra-attention to capture the sentiment features relevant among each word. In addition, we use the aspect-sentiment pair attention to represent the sentiment features of the document under the specific aspect information and sentiment features in the inter-attention module. Experiments on four real-world datasets show that our proposed model outperforms the state-of-the-art baselines in the prediction accuracy.
The Strategy of Beating the Intermediate Basis Attack in Quantum Communication Networks

Quantum communication network is a new type of secure communication technique and has drawn a lot of attentions in recent years, it has absolute safety in theory. However, quantum communication networks can still be attacked in different ways, among which the intermediate basis attack based on intercept-resend is a typical eavesdropping strategy. With this method, The probability of the eavesdropper correctly guessing the sender's code value can reach up to 0.854, resulting in the quantum bit error rate (QBER) of 0.25. To improve the security performance of quantum communication networks, we propose a strategy based on attack basis detection for beating the intermediate basis attack named “WN19”. In WN19, we can reduce QBER and the probability of the eavesdropper obtaining information correctly by adjusting the initial state of the quantum state of the sender according to the result of attack basis detection. The simulation results show that if the polarization angle $\theta$ of the attack basis is $\pi/8$, the QBER reduces from 0.25 to 0.1367 and the probability of eavesdropper correctly obtaining information decreases from 0.854 to 0.5811. It effectively improves the security of quantum cryptography under intermediate basis attack and provides a theoretical basis for the healthy development of quantum communication system.
Model voyager

This paper presents the Model Voyager, a web-based application for visualizing user interface models structured according to the four abstraction levels of the Cameleon Reference Framework: tasks and concepts, abstract user interface, concrete user interface, and final user interface. This application enables the designer to collect, edit, and manage collections of user interface models for a project or for maintaining an accessible catalogue of models, along with their illustrations. It also introduces three deployment mechanisms: multi-reification, multi-abstraction, and multi-translation. This paper demonstrates the applicability of the Model Voyager on the "car rental" case study, a reference example chosen by the W3C group on model-based user interfaces.
Analysis Of Soa Framework For Real Estate Industry

Abstract This study focus to analysis business model for SOA framework implementation in property in real estate commerce. It cause of the improvement of revolution industry for support real estate industry. Although many of e-commerce has been support real estate industry, however there is a challenge to build SOA framework to improve it. Research method in this study is observe the phenomenon, literature review, and analysis business model with business canvas model. We proposed the business model and analysis system requirement of SOA framework to support real estate industry. We create recommendation to integrate the ecosystem in the real estate industry which is different and various platform and applications. It cause the integration is needed and to improve the seamless integration.
Visualization of Automated Material Handling System Components in Semiconductor Industry over the Lifecycle

Automated Material Handling Systems are highly complex which makes planning, monitoring and optimization a challenging task. Operators of an Automated Material Handling System (AMHS) have access to extensive data from the real system and additional data sources like material flow simulation studies. Until now, these data have mainly been evaluated statically in the form of predefined diagrams and reports. The aim of this article is to present a concept that allows interactive data visualization of semiconductor transport systems from planning up to their operational phase. Thereby the application of approaches from the field of Visual Analytics is of special interest. A high potential for a better understanding of the highly complex AMHS is seen here.
Building a portable deeply-nested implicit information flow tracking

Dynamic Information Flow Tracking has been successfully used to prevent a wide range of attacks and detect illegal access to sensitive information. Most proposed solutions only track the explicit information flow where the taint is propagated through data dependencies. However, recent evasion attacks exploit implicit flows, that use control flow in the application, to manipulate the data thus making the malicious activity undetectable. We propose NIFT - a nested implicit flow tracking mechanism that extends explicit propagation to instructions affected by a control dependency. Our technique generates taint instructions at compile time which are executed by specialized hardware to propagate taint implicitly even in cases of deeply-nested branches. In addition, we propose a restricted taint propagation for data executed in conditional branches that affects only immediate instructions instead of all instructions inside the branch scope. Our technique efficiently locates implicit flows and resolves them with negligible performance overhead. Moreover, it mitigates the over-tainting problem.
LightSpace: An Interactive, Projection-Mapped (doll)House

The home of the future will be an interface (in itself) much like current, omni-present screen-based, interfaces. Designing the interior of your home will be as gratifying and immediate as a click is today, with modular digital furniture becoming the status quo and their design democratized and in the hands of users.
GIUPlayer

We developed a gaze immersive YouTube player, called GIUPlayer, with two objectives: First to enable eye-controlled interaction with video content, to support people with motor disabilities. Second to enable the prospect of quantifying attention when users view video content, which can be used to estimate natural viewing behaviour. In this paper, we illustrate the functionality and design of GIUPlayer, and the visualization of video viewing pattern. The long-term perspective of this work could lead to the realization of eye control and attention based recommendations in online video platforms and smart TV applications that record eye tracking data.
Sony Alpha A9 vs A9 II: the key differences between Sony’s sports-oriented cameras

The Sony Alpha A9 II has some updates in comparison to its predecessor -- here's what's different.
Intratextual Factors in Source Text Trimming for Indonesian-English Machine Translation

Abstract Source text analysis is a crucial phase in the translation process. Several issues are proposed by experts in this particular field of study. Nord [1], for instance, proposes that this phase may be constrained by two types of factors, i.e. extra-textual and intra-textual ones, the latter consisting of eight factors which need consideration, i.e. subject matter, content, presuppositions, text composition, non-verbal elements, lexis, sentence structure, and suprasegmental features. This paper aims to consider the importance of each of the eight factors as the basis of trimming academic manuscripts in Indonesian before their mechanical rendering to English for international publication. The object of this study was 50 manuscripts in Indonesian prepared for the publication. The manuscripts were analyzed taking the eight factors into account. The trimming process, which refers to a process of making a text tidier for inter-lingual rendering, is applied to the texts considering the issues so as to be more ready for the translation. The effectiveness of the process is shown by means of comparing the results of rendering the original texts and the trimmed ones assuming that the latter is more qualified than the former. Given that the intra-textual factors are relatively influential in source text analysis for trimming purposes, it is suggestible to consider them as a supplementary procedure for the mechanical rendering of academic manuscripts in Indonesian prior to their translation to English.
iPhone 11 Pro Max Review – A Big Powerful Phone

The iPhone 11 Pro Max is a big, powerful phone, its the biggest, most powerful phone that Apple has ever made. Pros ...
Instance Weighted Clustering: Local Outlier Factor and K-Means

Clustering is an established unsupervised learning method. Substantial research has been carried out in the area of feature weighting, as well instance selection for clustering. Some work has paid attention to instance weighted clustering algorithms using various instance weighting metrics based on distance information, geometric information and entropy information. However, little research has made use of instance density information to weight instances. In this paper we use density to define instance weights. We propose two novel instance weighted clustering algorithms based on Local Outlier Factor and compare them against plain k-means and traditional instance selection.
Review Study on software risk factors

Risk management is an important aspect of any software project management. Risk management conditions are a position that prevents the software project from be a successful and within time or intervenes to make a project late. This is a critical aspect of software product development, where it is often ignored. From that, software project development schedules are negative influenced by managers themselves pressing their employees to overcome time losing problem when it was planned to be properly planned. Each software project faces risks of all types and seriousness. Design flaws nearly cause 50 percent of security problems, so the analysis of risk at the design stage is very important issue of the software security. Risk analysis is also a means of elicitation the required data to make a good decision depend on knowledge about sensitivity parts, threats and impacts. Most of the risk analysis methodologies share some common principles and limitations but also have advantages and disadvantages when implemented to recent software design.
A Privacy-Preserving Framework for Collecting Demographic Information

Currently one of the biggest challenges regarding demographic detection in images and social media is the lack of labelled demographic data. A big part of the challenge is that no suitable mechanism exists to replace traditional intercept surveys in a way that ensures fairness and inclusion. The lack of labelled data has also impacted the training of AI algorithms. That is the lack of labels relevant to the target domains has made it hard to estimate the accuracy of the AI algorithms when they are applied to real world situations. In this paper, we propose a framework for collecting in-the-wild images and demographic labels from ordinary people (e.g., park visitors) that ensures that privacy is integrated at every stage of the data collection process from storage to processing and sharing.
Hybrid Entropy Method for Large Data Set Reduction Using MLP-ANN and SVM Classifiers

Data reduction has an extreme role in machine learning and pattern recognition with high-dimensional data. It can be achieved with attribute reduction and/or instance selection. The selection of relevant instances and/or attributes from the large amount of data not only affects data reduction (size) but also maintains classification accuracy. Different algorithms have been proposed by many researchers based on instance based learning for attribute reduction to improve classification accuracy. In this paper, based on entropy and fuzzy entropy attribute reduction method is introduced and further tested with different kinds of classifiers. Proposed method helps in reducing storage size and improving classification accuracy. A large number of standard data sets with multiple classes are used to investigate the performance of the proposed method. The performance of this novel approach in terms of reduced data size and mean classification accuracy is measured and compared with existing instance based learning and attribute reduction methods. The experimental result reveals that the performance of the proposed method is positively better than current methods.
Effectiveness of Propaganda Influence: Methodology and Challenges of the Internet Space

The article analyzes the efficiency criteria that underlie on a number of theoretical and methodological approaches. The history of this research question goes back more than a decade. At the same time, the conditions of the digital environment require a transformation of the evaluation effectiveness of propaganda content. Also, it has a connection with the peculiarities of communication processes in the Internet space.