Reviewerly/exHarmony huggingface dataset:
Differentially Private Simple Linear Regression

Abstract Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individual information. We study regression algorithms that satisfy differential privacy , a constraint which guarantees that an algorithm’s output reveals little about any individual input data record, even to an attacker with side information about the dataset. Motivated by the Opportunity Atlas , a high-profile, small-area analysis tool in economics research, we perform a thorough experimental evaluation of differentially private algorithms for simple linear regression on small datasets with tens to hundreds of records—a particularly challenging regime for differential privacy. In contrast, prior work on differentially private linear regression focused on multivariate linear regression on large datasets or asymptotic analysis. Through a range of experiments, we identify key factors that affect the relative performance of the algorithms. We find that algorithms based on robust estimators—in particular, the median-based estimator of Theil and Sen—perform best on small datasets (e.g., hundreds of datapoints), while algorithms based on Ordinary Least Squares or Gradient Descent perform better for large datasets. However, we also discuss regimes in which this general finding does not hold. Notably, the differentially private analogues of Theil–Sen (one of which was suggested in a theoretical work of Dwork and Lei) have not been studied in any prior experimental work on differentially private linear regression.
A novel registration‐based algorithm for prostate segmentation via the combination of SSM and CNN

Precise determination of target is an essential procedure in prostate interventions, such as prostate biopsy, lesion detection, and targeted therapy. However, the prostate delineation may be tough in some cases due to tissue ambiguity or lack of partial anatomical boundary. In this study, we propose a novel supervised registration-based algorithm for precise prostate segmentation, which combines the convolutional neural network (CNN) with a statistical shape model (SSM).The proposed network mainly consists of two branches. One called SSM-Net branch was exploited to predict the shape transform matrix, shape control parameters, and shape fine-tuning vector, for the generation of the prostate boundary. Furthermore, according to the inferred boundary, a normalized distance map was calculated as the output of SSM-Net. Another branch named ResU-Net was employed to predict a probability label map from the input images at the same time. Integrating the output of these two branches, the optimal weighted sum of the distance map and the probability map was regarded as the prostate segmentation.Two public data sets PROMISE12 and NCI-ISBI 2013 were utilized to evaluate the performance of the proposed algorithm. The results demonstrated that the segmentation algorithm achieved the best performance with an SSM of 9500 nodes, which obtained a dice of 0.907 and an average surface distance of 1.85 mm. Compared with other methods, our algorithm delineates the prostate region more accurately and efficiently. In addition, we verified the impact of model elasticity augmentation and the fine-tuning item on the network segmentation capability. As a result, both factors have improved the delineation accuracy, with dice increased by 10% and 7%, respectively.Our segmentation method has the potential to be an effective and robust approach for prostate segmentation.
Joint instancewise and instance-union fusion for improving motion detection algorithms

Motion detection (MD) is a fundamental step in many advanced computer vision applications, but the various complex challenges in real surveillance videos lead to some false positives and false negatives in the detection results of traditional MD algorithms. Therefore, joint instancewise and instance-union fusion for improving MD algorithms, in which an instance segmentation model is combined with a traditional MD algorithm. are proposed to address this problem. First, for each input frame (indexed by t), the MD algorithm produces a binary mask Mt, and the instance segmentation model produces the specific categories of binary instance masks (BIMs). Second, according to the instance confidence, BIMs are divided into high-quality binary instance masks (HBIMs) and low-quality binary instance masks (LBIMs). Then instancewise fusion of HBIMs with Mt and instance-union fusion of LBIMs with Mt are used to generate a high-quality foreground segmentation mask DtH and a low-quality foreground segmentation mask DtL, respectively. Finally, the bitwise logic addition operation of DtH and DtL produces a more accurate foreground segmentation result than Mt, called Dt. The experimental results show that our proposed method with visual background extractor and YOLACT++ processes at a resolution of 320 × 240 videos at 30 frames per second. For the Changedetection.net -2014, SBM-RGBD, and labeled and annotated sequences for integral evaluation of segmentation algorithms datasets, the highest overall F-measure of our experimental results with our proposed method are 0.8454, 0.8094, and 0.8939, respectively, surpassing state-of-the-art unsupervised MD methods.
Natural Language SQL Query Processing using Fuzzy Matching and Elimination Technique

In Structured Query Language (SQL), complex queries are difficult to write or understand by a user, because every user is not familiar with SQL. A common user can able to retrieve the information from the query databases using natural language is considered as an important research area. To improve the communication between databases application and naive user, an enhanced application with intelligent interface are needed. A fuzzy system with matching and elimination technique is designed in this research study, where SQL queries are formed from the input given by the user through several steps like noise removal, lexicon normalization and query formation. Then, the system uses the Latent Dirichlet Allocation (LDA) to extract the keywords from the input query. Finally, matching and elimination techniques are used to find the data, which is related to the input query given by end-user. When compared with the existing SQL techniques, the proposed fuzzy method achieved 91% and 90.5% accuracy, 95% and 93% precision, and 0.10 and 0.12 error rate for both 28 and 50 queries.
Virtual Reality in ESL Teacher Training: Practical Ideas

The use of technology, such as Virtual Reality (VR), has started to take place in K-16 classrooms. Research indicates potential advantages of embedding VR into the curriculum in terms of higher motivation, increased self-expression, better achievement in content knowledge, and improved linguistic knowledge and higher order thinking skills. As a response to the call that more effort is needed to examine how teachers use technology in working with ELs, this preliminary work attempts to shed some light for teacher educators by providing curriculum examples in which teacher candidates collaboratively design teaching materials using VR with K-12 ELs. Grounded in multimodality, Second Language Acquisition (SLA), sociocultural theory, and cooperative learning, the paper explains the relationship between theories and practices, provides a literature review on the use of VR in education, and shares curriculum examples. The paper discusses how VR may be implemented in K-12 ESL classroom settings and reminds teacher educators to take consideration of a variety of factors.
A Physics-Informed Operator Regression Framework for Extracting Data-Driven Continuum Models.

and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.
Gamorithm

Examining games from a fresh perspective, we present the idea of game-inspired and game-based algorithms, dubbed gamorithms.
Design of automatic response system for handheld mobile learning equipment based on augmented reality

In order to overcome the problems of inadequate reality and long response time in traditional learning equipment response system, this paper proposes an automatic response system for handheld mobile learning equipment based on augmented reality. The hardware of the system is composed of keys, LED indicator, wireless communication transmitting module, wireless communication receiving module and microcontroller module. The system software is introduced into augmented reality. The technology is implemented and applied by AR display, and the design of automatic response system for handheld mobile learning equipment is completed. The experimental results show that the response time of the system is shorter than that of the traditional automatic response system for mobile learning equipment, which proves that the system has improved the performance of the automatic response system for handheld mobile learning equipment.
Summary of the 5th International Workshop on Requirements Engineering and Testing (RET 2018)

The RET (Requirements Engineering and Testing) workshop series provides a meeting point for researchers and practitioners from the two separate fields of Requirements Engineering (RE) and Testing. The goal is to improve the connection and alignment of these two areas through an exchange of ideas, challenges, practices, experiences and results. The long term aim is to build a community and a body of knowledge within the intersection of RE and Testing, i.e. RET. The 5th workshop was held in colocation with ICSE 2018 in Gothenburg, Sweden. The workshop continued in the same interactive vein as the predecessors. We introduced a new format for the presentations in which the paper authors had the opportunity to interact extensively with the audience. Each author was supported by a member of the organization committee to prepare either an extensive demo, collect more data in form of a questionnaire or perform a hands-on tutorial. We named this new format \X-ray session". In order to create an RET knowledge base, this crosscutting area elicits contributions from both RE and Testing, and from both researchers and practitioners. A range of papers were presented from short positions papers to full research papers that cover connections between the two fields. The workshop attracted 27 participants and the positive feedback on the new format encourages us to organize the workshop the next year again.
A hybrid feature selection algorithm for big data dimensionality reduction

Data dimensionality causes problems like large storage requirements due to data redundancy, noisy data visualisation and high computational cost in analytical processes. Hence, reduction of high data dimension data set is the vital task in big data analytics. Feature selection is a dimensionality reduction approach that targets to reduce the high data dimensions into l subset by attaining a set of uncorrelated principal variables based on key features from the data source by eliminating redundant, noisy features and irrelevant. In the proposed work, ant colony optimisation (ACO) and quick branch and bound (QBB) based hybrid algorithm for feature selection is presented to improve the efficiency of feature selection in big data analytics. ACO was implemented for feature selection by observing real ants in their search of their food resources. QBB algorithm is used to partition the large data set into two partitions initially. These two algorithms are combined and implemented to reduce the data dimensionality based on feature selection approach. This algorithm was simulated and compared against existing feature selection algorithms in terms of a set of performance metrics such as precision, recall, F-score, classification accuracy, size of selected feature and proved the efficacy of proposed hybrid algorithm.
Data Visualization

This chapter introduces the concept of data visualization and its role in helping readers understand and interpret data and spot valuable information such as trends and exceptions. The different graphical elements and charts are introduced with examples of healthcare visualizations created using different software tools. This chapter overviews the key relationships in data that can be displayed and highlighted in graphs. The chapter then covers infographics and dashboards, which are visualization-rich tools that are increasingly being used in the healthcare industry. The chapter ends with guidelines for building good visualizations, an outline of software tools that can be used for creating visualizations, a conclusion, and a list of references.
Spam Diffusion in Social Networking Media using Latent Dirichlet Allocation

Like web spam has been a major threat to almost every aspect of the current World Wide Web, similarly social spam especially in information diffusion has led a serious threat to the utilities of online social media. To combat this challenge the significance and impact of such entities and content should be analyzed critically. In order to address this issue, this work usedTwitter as a case study and modeled the contents of information through topic modeling and coupled it with the user oriented feature to deal it with a good accuracy. Latent Dirichlet Allocation (LDA) a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold: constructing the dataset which serves as the ground-truth for analyzing the diffusion dynamics of spam/non-spam information and analyzing the effects of topics over the diffusibility. Exhaustive experiments clearly reveal the variation in topics shared by the spam and non-spam tweets. The rise in popularity of online social networks, not only attracts legitimate users but also the spammers. Legitimate users use the services of OSNs for a good purpose i.e., maintaining the relations with friends/colleagues, sharing the information of interest, increasing the reach of their business through advertisings.
Board 59: Coevolutionary-aided Teaching: Leveraging the Links Between Coevolutionary and Educational Dynamics

His research interests primarily focus on methods of natural computation, theory of coadaptive and coevolutionary computation, application of coadaptive methods for multiagent learning, and high performance / high throughput computing.More generally, he is interested in designing and applying effective learning algorithms and representations for generating and modeling robust heterogeneous, multiagent team behaviors.
Artificial Music Generation using LSTM Networks

Advancements in machine learning have minimized the gap of variation between human and algorithm composed music. This paper realizes a music generation system using evolutionary algorithms. The music generation is fully automated with no requirement of human intervention. Multiple music sample from a single dataset were used to the neural network. Software has been constructed to exhibit the results over various datasets. The proposed model is based on recurrent neural network with the input layer represents a measure at time T, and the output layer represents the measure at time T+1. The approach results in generation of new music composition by the system. Composition rules are used as constraints to evaluate the melodies generated by the novel neural network. Thus, the results are expected to evolve to satisfy the defined constraints. The proposed system of work would be capable of music generation without human intervention.
PSO-CF<sup>2</sup> Multi-Objective Mobile Robot Path Planning with PSO-PID end Position Regulation

In this paper, we propose the Particle Swarm Optimization (PSO) based approach of mobile robot path planning. It is the PSO based Canonical Force Field approach (PSO-CF2). The PSO optimization technique is applied here in the design of the shortest trajectory to the goal with the best collision avoidance. The PSO searches for the best combination of the CF2 parameters that minimizes the path length and maximizes the distance between the robot and obstacles. A PID position regulator is applied at the end of navigation to get an arrest as close as possible to the goal with the desired orientation. PSO is applied also in the research of the best coefficients of the PID regulator. Simulation results show the feasibility of our mobile robot path planning approach in different environments. Everywhere, it dresses a short and secure path to the goal without any sudden deviation while avoiding obstacles. Simulation results show also the quality of the robot arrest in the desired destination with the correct orientation.
Plaintext checkable encryption with check delegation revisited

Plaintext checkable encryption (PCE) is a new notion which provides an add-on checkability on traditional public key encryption. In general, PCE allows that given a target plaintext, a ciphertext and a public key, the specific check algorithm can verify whether the target plaintext is identical to the underlying plaintext of the ciphertext with the public key. With the new functionality of checkability, PCE cannot achieve the CPA security undoubtedly. To achieve the CPA security, we can change the system framework and then restrict that only the specific and authenticated checker can perform such the check algorithm. In addition, the sender who encrypts a message does not need to have any information or identity of any checker. In this paper, we revisit PCE with designated checker. We formally present two new definitions, PCEAC and PCEDC, and, we propose two constructions. Finally, an extension of the definition is formalised to capture checkability only for a set of plaintexts.
Rebuilding Cultural and Heritage Space of Corregidor Island using GPS-Based Augmented Reality

One of the biggest impact of Augmented reality was on the architectural and construction industries. The AR ability to design and construct beyond visualization by virtually walking through the completed model draws inspiration among the researchers the desire to preserve memories behind the war-ravaged buildings that have not been restored, and left as they were after the war in reverence to the Filipino and American soldiers . The software engineering tool , rational unified process , was used as a guide in developing the application with the aid of Android Studio, Unity, Vofuria, Lumion8 and sketch-up as software development tools. The application was tested and evaluated by IT experts and tourists of Corregidor using the SUS evaluation tool. Based on the overall satisfaction, usefulness and ease of used among the tourists of Corregidors is “Excellent” which means that the developed mobile applications is more immersive and engaging that will make tourist journey more exciting at the Corregidor Island. This application can assist tourist in experiencing more interactive, fun and engaging journey in Corregidor. Furthermore, this application can served as a mobile tour guide application made for the tourist of Corregidor Island that shall help them learn more about the history and the significance of Corregidor Island by the use of augmented reality to show the original setup of the ruins. The application will help NCCA cascade the historical information about the island and its significance during the war.
Enhanced Image Security using New Sea Lion Optimization Algorithm

Protection of digital data is the utmost requirement of the day. Everything in the world is being upgraded to electronic communication and which requires protection against data fraud. Data is nowadays not only text but image, audio video individually and sometimes together as multimedia files. Encryption algorithms protect data against attacks and hackers. This paper proposes a new Sealion Optimization algorithm for enhanced image security, analyses several recent developments in encryption and decryption algorithms and summarizes different approaches, their benefits and limitations.
Requirements and Design Consistency: A Bi-directional Traceability and Natural Language Processing Assisted Approach

phase in the Software Development Life Cycle (SDLC). The design phase follows it. Traceability is one of the core concepts in software engineering; it is used to follow updates to make consistent items. This paper aimed to cover consistency through bi-directional traceability between requirements and design phase in a semi-automatic way. The Natural Language Processing (NLP) was used to analyze the requirements text and generate a class diagram; then, the generated items can be traced back to requirements. We developed a novel process to support consistency and bi-directional traceability. To ensure our proposed process's practical applicability, we implemented a tool named as Requirements and Design Bi-directional Traceability (RDBT). RDTB receives textual format requirements, performs NLP tasks (Tokenization, Part-of-Speech Tagging, etc.), generates UML class diagram, and finally performs traceability management to ensure consistency of requirements and UML class diagram. The work evaluation reveals good results, which indicates it can be used efficiently as a guide to generate the UML class diagram semi-automatically and manage traceability.
MEM: a new mixed ensemble model for identifying frauds

In the social security system, there still exist wilful insurance frauds. In this paper, to address the insufficient stability and randomness of the traditional insurance fraud evaluation model, we propose a new classifier called mixed ensemble model (MEM). Based on the principle of ensemble learning, MEM combines several different individual learners and uses Q statistical methods to evaluate diversity. MEM has been tested on two fraud related datasets to compare with three state-of-the-art classifiers: neural network, naive Bayes and logistic regression. The experimental results show that MEM performs better than the other three classifiers in both datasets under the four measures: accuracy, recall, F-value and kappa. MEM can be a useful method for the detection of social insurance fraud.
Semi-supervised dimensionality reduction based on local estimation error

The construction of a graph is extremely important in graph-based semi-supervised learning. However, it is unstable by virtue of sensitivity to the selection of neighbourhood parameter and inaccuracy of the edge weights. Inspired by the good performance of the local learning method, this paper proposes a semi-supervised dimensionality reduction based on local estimation error (LEESSDR) algorithm by utilising local learning projections (LLP) to semi-supervised dimensionality reduction. The algorithm sets the edge weights through minimising the local estimation error and can effectively preserve the global geometric structure as well as the local one of the data. Since LLP does not require its input space to be locally linear, even if it is nonlinear, LLP maps it to the feature space by using kernel functions and then obtains its local estimation error in the feature space. The effectiveness of the proposed method is verified on two popular face databases with promising classification accuracy and favourable robustness.