Reviewerly/exHarmony huggingface dataset:
Social Impact Of The Development Of Suai-Beasu Toll Road Infrastructure Megaproject

In This Study The Researcher Chose The Topicsocial Impact Of The Development Of The Toll Road Infrastructure Megaproject With The Purpose To Determine The Situation Of The Social Impact Caused During The Implementation Of The Suai-Beasu Toll Road Infrastructure Megaproject Development. The Problems Faced By The Community Needed To Involve Stakeholders In Decision Making Process And A Number Of Alternatives Are Used For Solutions. These Problemsare Due To The Different Characteristics Of Society Which In Turn Have Caused Conflicts Of Opinions. To Resolve These Conflicts Stakeholders Choose Alternatives That Are Considered The Best And Is Accepted By All Parties. Subarsono (2005: 23) Explains That The Formulation Of Policy Issues Is Crucial, But All Of These Do Not Get Enough Attention. Furthermore, The Modernization Theory Explains That In General, Underdeveloped Countries Will Follow The Same Path As Developed Industries In The West So That Later On They Will Also Become Developing Countries Through The Modernization Process (Light, Et Al, 1989). However, This Theory Emphasizes That In Essence An Underdeveloped Society Needs To Overcome Its Various Shortcomings And Issues So That It Can Get To The Take Off Stage Towards An Economic Development.                  This Research Method Uses Descriptive Qualitative Research, Because The Qualitative Approach Is A Process Of Research And Understanding Which Is Based On The Methodology That Investigates A Social Phenomenon And Human Problems. To Obtain This Data, According To Miles Hubermen, Activities In Qualitative Data Analysis Must Be Carried Out Interactively And Continuously Until Valid Data Is Obtained. So In A Process Like This There Is What Is Called Data Reduction, Data Display, And Conclusion Drawing / Verification (Sugiyono 2017: 246-252). The Results Of This Study Found That There Was A Conflict Of Opinions Among The Community Due To The Lack Of Socialization Of Information About The Construction Of The Supply Base And The Construction Of Toll Road, Which Led To Pros And Cons Between Land Owners And Stakeholders And Workers As Victims Of Road Construction Who Did Not Have Any Attention From The Government. Other Experiences Faced Were That The Relevant Implementing Ministries Of The Toll Road Megaproject Contruction Were Blaming On Each Other On Flaws Occurred.
Global Challenges of Mobile Communication

Mobile communication has been deployed as an important form of communication by transnational businesses as well as by individuals throughout the world. While it has performed wonders for individuals, societies, and nations worldwide, it has also created numerous challenges or issues. This paper surveyed and discussed six of such challenges or issues: interruption of traditional business models, threats to privacy, cyberbullying, unethical behavior in the workplace, undesirable impact of texting, and online piracy.
Semi-Supervised Multi-View Clustering with Weighted Anchor Graph Embedding

A number of literature reports have shown that multi-view clustering can acquire a better performance on complete multi-view data. However, real-world data usually suffers from missing some samples in each view and has a small number of labeled samples. Additionally, almost all existing multi-view clustering models do not execute incomplete multi-view data well and fail to fully utilize the labeled samples to reduce computational complexity, which precludes them from practical application. In view of these problems, this paper proposes a novel framework called Semi-supervised Multi-View Clustering with Weighted Anchor Graph Embedding (SMVC_WAGE), which is conceptually simple and efficiently generates high-quality clustering results in practice. Specifically, we introduce a simple and effective anchor strategy. Based on selected anchor points, we can exploit the intrinsic and extrinsic view information to bridge all samples and capture more reliable nonlinear relations, which greatly enhances efficiency and improves stableness. Meanwhile, we construct the global fused graph compatibly across multiple views via a parameter-free graph fusion mechanism which directly coalesces the view-wise graphs. To this end, the proposed method can not only deal with complete multi-view clustering well but also be easily extended to incomplete multi-view cases. Experimental results clearly show that our algorithm surpasses some state-of-the-art competitors in clustering ability and time cost.
Blockchain-based Bidirectional Transformations for Access Control and Data Sharing in EMRs

Electronic medical records (EMRs) are scattered in different hospitals, which hinders the process of data sharing. On the other hand, people with different roles should access different parts of data, thus we need a way to control the accessibility. To address these issues, we propose a blockchain-based data sharing system that gathers EMRs into blockchain and controls data sharing through bidirectional transformation. In our system, EMRs are encoded with a carefully designed data structure that stores not only data, but also data's read/write permission for different roles. We redesigned a bidirectional transformation language based on our previous work BiGUL by taking access control into consideration, and the accessibility are checked during program execution in both direction to avoid un-authorized data access. Further more, each bidirectional transformation program and updates on the shared data are stored in the blockchain in a transaction-like form. The immutability of blockchain guarantees EMR's data integrity.
Human Detection in Still Images Using Hog with SVM: A Novel Approach

Human detection in images gets more popular for applications includes night vision, robotics, surveillance etc. Existing systems in the literature support detecting single and multiple human objects. Algorithms developed for detecting humans in occluded condition have many troubles due to the problem of occlusion. This paper proposes a method to detect human objects in both multiple and occluded condition. To address the above problem, this method uses HOG for describing features and SVM for classifying objects. To examine the proposed algorithm, different set of images are used which consists of human and non-human objects like buildings, trees, lawns and roads. Then the features are obtained in the image dataset and assign the labeling to make a difference between human and non-human images. Thereafter feature vectors and labels are stored as a model for training the detector. SVM is used to train the detector. The feature vectors and labels are fed into the SVM to get trained. It cuts the image into slides and processes it for feature extraction. The detector uses the trained model for classifying new images for detection.
Learning Based Contrast Enhancement Evaluation Using Cartoon Texture Decomposition

Contrast enhancement is an important processing task needed to improve the overall perceptual quality of an image. Ideally, a contrast enhancement method should neither affect the naturalness of images nor introduce other unwanted artifacts when improving perceptual quality. While humans are able to detect any improvement or such unwanted side-effects relatively easily, designing an objective metric that does the same is still a challenge. Most existing contrast enhancement evaluation (CEE) metrics do not take into consideration all these important criteria when evaluating quality. In this work, we propose a new measure for CEE that combines some of the carefully selected existing metrics effectively. Each of the selected metrics tackles only one of the important criteria of an effective contrast enhancement method such as structure preservation or contrast improvement. For this novel fusion-based metric, we have further exploited the full potential of the selected metrics by applying them on decomposed image using cartoon-texture decomposition. The results on two CEE databases show the effectiveness of our proposed metric.
PARALLEL IMPLEMENTATION OF THE YANENKO METHOD FOR SOLVING THE HEAT EQUATION

In this article, we will consider the parallel implementation of the Yanenko algorithm for the two-dimensional heat equation, and the sweep method was used to numerically solve the heat equation. The implementation of the sequential program is carried out simply in two-part steps by the longitudinal-transverse run, however, parallelization of two fractional steps with an indefinite scheme is difficult due to the creation of inter-process communication of data. In the course of the study, a parallel data distribution with one-dimensional decompositions is shown in the application of the Yanenko method for calculating heat conductivity. The results of parallelization of this task using the 1D decomposition were obtained and acceleration and efficiency images were analyzed in order to evaluate the parallel program. Currently, modeling of processes by numerical solution of differential equations is widely used in every field of Science, the most common methods bring the differential problem to a system of linear algebraic equations, methods that solve such systems include various startup options. The emergence and development of computing systems using Multi-Core processors and graphics accelerators make the problem of startup parallelization relevant; the results of the study are used for teaching in research institutes and universities.
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning

This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning.Our system uses a large pre-trained language model as the encoder and an additional dual multi-head coattention layer to strengthen the relationship between passages and question-answer pairs, following the current state-of-the-art model DUMA.The main difference is that we stack the passage-question and question-passage attention modules instead of calculating parallelly to simulate re-considering process.We also add a layer normalization module to improve the performance of our model.Furthermore, to incorporate our known knowledge about abstract concepts, we retrieve the definitions of candidate answers from Word-Net and feed them to the model as extra inputs.Our system, called WordNet-enhanced DUal Multi-head Co-Attention (WN-DUMA), achieves 86.67% and 89.99% accuracy on the official blind test set of subtask 1 and subtask 2 respectively.
The Obfuscation Method of User Identification System

The research on the identification of network users has been continuous, and methods and algorithms emerge in endlessly. Of course, it solves the problem that it is difficult to identify the identity of network interactive users. Yet, user identification raises questions about user privacy. In the last paper [[, we proposed a user identification method based on Mining Web Usage Profiles from Proxy Logs. For the previous user identification system, we study that which degree the surfing behavior must be obfuscated to prevent identification. Furthermore, we investigate directions in how far a user can hide her identity by obfuscating her web usage pattern through adding or hiding random HTTP connections. In experimental evaluation, we examine the time-period being necessary to obfuscate an identity based on a two-week log file being provided by our industrial partners.
A STUDY INTO DESIGNING AN AMBIENT WATER BOTTLE THAT SUPPORTS USERS' WATER-INTAKE TRACKING PRACTICES

Abstract Taking on the challenge of motivating users to drink water regularly, we designed a smart water bottle that can track water intake behavior and inform users about this behavior through ambient feedback. We then conducted two studies to explore the bottle's feedback design from the perspective of users and designers. First, we conducted semi-structured interviews with 10 prospective users and found that they would like to receive personalized, precise, gamified and reminding feedback. Second, we conducted a design workshop with 13 professional designers to explore the range of visualizations that can be used to give feedback. Analyzing these visualizations, we identified three reminder types (augmenting, restoring and balancing) and six visualization styles grouped according to three dimensions of ambient displays (representation fidelity, notification level, aesthetic emphasis). In this paper, we first explain our water bottle concept along with existing solutions. Then, we report the results of these studies. Finally, we discuss the potential implications of the results for our own work as well as for designing ambient displays aimed at supporting users' water intake tracking practices.
Extrinsic Evaluation of Cross-Lingual Embeddings on the Patent Classification Task

In this article we compare the quality of various cross-lingual embeddings on the cross-lingual text classification problem and explore the possibility of transferring knowledge between languages. We consider Multilingual Unsupervised and Supervised Embeddings (MUSE), multilingual BERT embeddings, XLM-RoBERTa (XLM-R) model embeddings, and Language-Agnostic Sentence Representations (LASER). Various classification algorithms use them as inputs for solving the task of the patent categorization. It is a zero-shot cross-lingual classification task since the training and the validation sets include the English texts, and the test set consists of documents in Russian.
Efficient Revocable Attribute-Based Encryption from R-LWE in Cloud Storage

With the development of quantum computing, the cloud storage environment may face quantum algorithm attack. Recently, several scheme are proposed that combine encryption algorithms under the hardness of lattice problem with the structures of Attribute-based Encryption (ABE) to achieve resistance to quantum computing attacks. However, these schemes cannot realize secure user attribute revocation. To solve this problem, we construct an Efficient Revocable Attribute-based Encryption from R-LWE scheme (RABE-RLWE) in cloud storage. Our scheme is able to withstand quantum algorithm attack, and achieve data fine-grained access control. Moreover, RABE-RLWE scheme is able to achieve user attribute revocation by controlling user access rights and renew user attribute. In addition, under Ring-Learning with Error(R-LWE) assumption, it is proved that RABE-RLWE scheme is able to withstand chosen plaintext attack (CPA) under R-LWE assumptions. Finally, our scheme is more efficient than other related schemes.
Cognitive Living Spaces by Using IoT Devices and Ambient Biosensor Technologies

In the near future, our deeply connected and fully digitized physical facilities require cognitive processes that are embodied in a regular sensing of living environments. Pervasive sensor networks will enable the development of more efficient technologies that will integrate Artificial Intelligence based services better into psychosocial and human ecological contexts. These innovative mixed and integrated biophysical and digital living spaces enable to use them more efficiently, conveniently, and, furthermore, to interpret these new intelligent environments in a completely different way. Based on the growing insight into relations between human beings and their surroundings, residents get an overview of their building ecosphere. They obtain the potential to develop strategies in context with intelligent buildings that are enabled to assist in changing behaviors in everyday life. We study human factors, in particular, the potential of change in human behavior in such fully integrated services, strongly related to our living and work place in the case of home office. After using these sentient cognitive environments, we explore novel ways of interacting with living and work spaces by offering opportunities to give intelligent environments a virtual voice and representation via the digital data space. During the COVID-19 pandemic, people should pay more attention to their immediate living and working environment: it becomes more important to monitor the quality of the air to breathe, surroundings are made visible to get to know them better, cope better, enjoy them, etc. The presented work provides in this context quantitative data from novel low-cost biosensors, such as for measuring carbon dioxide concentration distribution, highlighting the presence and attention of residents and their change in behavior within a sample living space, and also provides conclusions towards novel research pathways for integrating cognitive processes into a network of IoT devices and ambient biosensor technologies.
Data Cleaning of Binary Sensor Events in Activity Recognition by Cluster-Based Methods

The Ambient Assisted Living (AAL) systems use sensors to detect the daily behavior of older adults and provide necessary assistance based on changes in their cognitive status and physical functions, thus enabling older adults to maintain their independence at home. However, the effectiveness of the AAL systems depends on the accuracy of the data provided by sensors. Namely, when a human error or a hardware failure occurs, the activity recognition model can become inaccurate. This inaccuracy hinders the identification of critical and potentially life-threatening activities. Although there are many methods for cleaning sensor data, there is no method for binary sensors deployed in smart homes. By considering noisy sensor events and unintentional forgetting of turning off the device, this paper proposes two clustering-based methods for denoising and splitting binary sensor events to address possible inaccuracy due to the two mentioned problems. The effectiveness of the proposed methods is verified by the experiments using four machine learning models and three real-world smart home datasets and adopting different sensor configurations. The experimental results demonstrate that compared to the original unprocessed datasets, by combining the two proposed methods, the average accuracy and F-measure are improved by 15.00% and 17.25%, respectively.
ASPDup: AST-Sequence-based Progressive Duplicate Code Detection Tool for Onsite Programming Code

Duplicate code is an example of bad smells, which are usually been refactored after the detection to improve the quality of programs. Locate the duplicate code at the programming phase may reduce the cost of maintenance, but the challenge is it need to detect duplicate code between an incomplete code fragment with complete files, which the existing tools are hard to be applied to this scenario. In this paper, we propose an AST-sequence-based duplicate code detection approach for onsite programming code. The abstract syntax tree (AST) is extracted from source code and then is transformed into an encoded sequence. A local sequence alignment algorithm is used to find highly similar subsequences. After the post-processing, similar regions will be found between two code fragments according to the subsequences. We have developed a prototype tool as a plugin for Visual Studio Code. Experimental results indicate that our approach is effective in finding highly similar regions between cross-granularity code fragments, which can facilitate duplicate code detection for incomplete onsite programming code.
Toward Automated Mixed Reality Interface Design: An Evolutionary Optimization Approach

In this research, we propose an innovative capsule based optimization model toward the automated user interface (UI) design of mixed reality devices. Within the model, functional, spatial, and cognitive seams unique to mixed reality devices are considered as the constraints of the capsule, the unit cohort of UI elements. A genetic evolutionary optimization solution approach was used to solve the optimization problem. The results demonstrate that the number of optimal UI elements per capsule is linearly proportional to the initial estimate of the UI elements of the capsule. The total reward of the capsule design is also linearly proportional to the number of optimal UI elements per capsule. The identified linear relationship model suggests that it is feasible to predicate the optimal number of UI elements therefore achieving the automated design of the mixed reality interface. Experimental results have validated the proposed optimization framework suggesting that the optimization reward objective function is able to match app ratings from the app store. The seam constraints of the optimization function are also consistent with the time taken to complete the task which shows the validity of using seam to model the automated mixed reality interface design.
Design of High Performance ALU Using Vedic Mathematics

Abstract This article proposes a Vedic multiplier-based design of multiply and accumulate unit by employing UrdhvaTiryagbhyam Sutra. Further, it implements an efficacious ALU with 32-bit architecture. Simulation analysis disclosed the comparison of proposed 32-bit ALU with existing architectures. In addition, complexity in hardware, area and delay reduction demonstrated that proposed 32-bit ALU is more efficient over conventional architectures.
A Time-Optimal Trajectory Algorithm Based on Accessibility Analysis

In order to make industrial robots move faster, more accurately and more safely along a predetermined path [1], a time-optimal trajectory algorithm based on accessibility analysis is designed in this paper. This algorithm adopts a widely used path-velocity decomposition framework [2], introduces path parameters, changes the velocity and acceleration of the trajectory by changing the path parameters, and then completes the planning in Cartesian space. This paper discusses the problem of the number of interpolation points in the path [3], and puts forward the principle of coarsening the interpolation points according to the length of the path, and then supplementing the interpolation points according to the curvature and the planned acceleration. Make interpolation point distribution more reasonable. Then, the positive and negative search algorithm is proposed to determine the deceleration point, which greatly improves the efficiency compared with the traditional iterative search algorithm [4] and the method of finding the switching point [5]. Finally, the program of the straight line, arc, and spline curve is written to verify the effect of the algorithm.
The Method of Collision Risk Assessment Using Soft Safety Domains of Unmanned Vehicles

This work presents a new method of real-time approximate volumetric collision risk assessment relating to the joint motion of a large group of unmanned vehicles in confined areas. The authors propose the novel concept of multi-level dynamic soft safety domains and spherical dynamic soft topology that allows defining non-spherical safety domains by measuring various radiuses within sectors located in different longitude and latitude. The nonlinearity of the proposed spherical topology allows us to use a novel volumetric approach to collision risk assessments. If the safety domains of various objects overlap at some point within joint motion space, their safety grades should be summed up with respect to the volumes of the cells. The proposed method builds the distribution of the collision risk over the space to prioritize risks properly. The proposed method is quite simple and fast, it allows unmanned vehicles to effectively assess the collision risks imposed by participants of the joint motion process in situations of very dense traffic of numerous vehicles. It provides the acceptable performance of collision risk assessment. The proposed collision risk assessment method is intended to be used in the real-time navigation support systems for large groups of unmanned vehicles to keep safety during cooperative trajectory planning and re-planning.