Reviewerly/exHarmony huggingface dataset:
Impact of Coding Styles on Behaviours of Static Analysis Tools for Web Applications

Web applications have become an essential resource to access the services of diverse subjects (e.g., financial, healthcare) available on the Internet. Despite the efforts that have been made on its security, namely on the investigation of better techniques to detect vulnerabilities on its source code, the number of vulnerabilities exploited has not decreased. Static analysis tools (SATs) are often used to test the security of applications since their outcomes can help developers in the correction of the bugs they found. The conducted investigation made over SATs stated they often generate errors (false positives (FP) and false negatives (FN)), whose cause is recurrently associated with very diverse coding styles, i.e., similar functionality is implemented in distinct manners, and programming practices that create ambiguity, such as the reuse and share of variables. Based on a common practice of using multiple forms in a same webpage and its processing in a single file, we defined a use case for user login and register with six coding styles scenarios for processing their data, and evaluated the behaviour of three SATs (phpSAFE, RIPS and WAP) with them to verify and understand why SATs produce FP and FN.
Navigating the Rough Seas of Software Project Resistance: Four Project Manager Personas to Help Bring the Ship Safely to Harbor

We analyze software project resistance by focusing on its nature, object, and stage of development. Our results highlight four archetypical personas-coach, doctor, politician, and priest-that software project managers can assume when their projects face resistance.
Key Concepts of Fuzzy Sets

This chapter covers the main concepts of fuzzy sets, offers illustrative examples and brings some algorithmic developments essential to the characterization and processing fuzzy sets.
Classifying WLAN Packets from the RF Envelope

This paper describes Packet Assay, a power efficient sparse neural network (NN) that can discriminate between wireless transmissions, such as WLAN packets, based solely on the RF signal envelope, a feature that can be measured with much less power than fully demodulating and decoding the packets. The NN was trained on a Wireless Local Area Networks (WLAN) dataset developed in-house with over 600K labeled samples and achieved above 88% accuracy while maintaining a memory footprint of only 4.9KB. This approach can reduce the power consumption of wireless modules (WM), can minimize the signal processing in IoT devices, and provides a foundation for future protocol development.
Supply Chain Distribution Strategy

Abstract Distribution strategy deals with the journey of products, from the completion of production all the way to the hands of customers. It aims to get the products to the right place at the right time, in the right quantity, and at the right price. An effective distribution strategy requires a distribution network that balances the distribution cost with service level. The connectivity empowered by the Internet and particularly mobile broadband accelerates the information exchange in the supply chain, enables new business models, and demands a more connected, customer-driven, and responsive distribution network. Empowered by data technologies, the distribution network is becoming increasingly proactive and integrated. More research is called on to study the new challenges and research issues resulting from technological advancements and new business models.
A Proposed Method for Feature Extraction to Enhance Classification Algorithms Performance

Classification is one of the main tasks of machine learning. A proposed method to improve the accuracy of classification by adding new six features to the original dataset is proposed in the paper. These new six features are extracted from supervised and unsupervised set of algorithms. The decisions of three different classification algorithms and three different clustering algorithms are added as new features. These algorithms are selected with a condition of having various techniques to build models and in order to guarantee diversity. Moreover, three scenarii are proposed to generate the new features. Then, simple voting algorithm is applied to the new dataset (i.e. original dataset and new added features). The experimental results on 10 different datasets from UCI repository and NSL-KDD dataset proved that the proposed features ranked better than original features and the accuracy enhanced by about 12%.
Efficient top representative for multi-authorship encrypted cloud data to assist cognitive search

Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage.
Sistem Pakar Perhitungan Pajak Penghasilan Berbasis Android

Taxes are one of the most important state revenues in the construction of infrastructure and the development of state registration. The tax is coercive and the tax that applies to employees is PPh 21 tax in which the income of an employee is deducted by a company authorized by the government to deduct the income received by the employee. But for the calculation of income tax there are two differences, namely those who have a NPWP and those who do not have a NPWP, who do not have a NPWP will be subject to a penalty of 120% which is regulated in tax law No. 36 of 2008, Article 21 paragraph (5a) of income tax received. with the development of increasingly sophisticated technology simulations become the key in calculating income tax can help employees to find out how much tax must be paid to the State and information on how to calculate taxes that do and do not have in accordance with applicable regulations. This simulation is designed to be an Android-based income tax calculation simulation application using the forward chaining method to help employees find out the amount of tax in the simulation of income tax calculation.
A View-Invariant Feature Learning Model for Cross-View Security Authentication in Mobile Smart Devices

Multi-view data is common in the real-world applications ever since many widely used mobile smart devices could capture and recording data from multi-perspective. One of the applications using multi-view data is for security authentication. Existing security authentication methods used by smart device show unacceptable recognition performance in most of the situations. To overcome the problem, this paper propose a view-invariant feature learning model, which learns a feature subspace to map the two-view data into a common space. Our model exploits two regularization terms constructed with low-rank constraint to dig the global class-structure and the view-invariant structure from different views. Therefore, the complementary information is preserved while redundant information can be removed from the two views. Besides, we develop a numerical iterative scheme to optimize the novel model. Experimental results prove that the proposed method outperforms other methods.
Demo Abstract: iCrutch: A Smartphone-based Intelligent Crutch for Smart Home Applications

As the rapid development of smart home applications, they bring us much more convenience to operate smart lights, room heaters etc., directly. However, it is still hard for the elderly or someone with leg problems to operate the above things because the movement of them is dependent on a pair of crutches so that their hands are not always available while using crutches. Therefore, if the elderly or someone with leg problems can control the devices directly with crutches, it will become more comfortable and convenient for them to live in the smart houses. Due to such motivation, we propose iCrutch in this paper. By bidding the user's obsolescent smartphone on the currently-used crutch, iCrutch can recognize the user's actions and send controlling commands to the smart home actuators for further response. Unlike remote controllers, our iCrutch permits the user to operate without leaving his/her hands from the crutches. Meanwhile, iCrutch almost does not introduce extra cost since the obsolescent smartphone and the current crutch are made full use of. The expense of our system is noticeably reduced compared with embedding a smart system into the crutch.
Intra Coding With Geometric Information for Urban Building Scenes

Regarding the interior non-local correlations of image content, the intra predictions in image/video coding standards have still not exploited them efficiently. In this article, we propose an intra coding method that leverages the geometric information extracted from the image content to strengthen the coding performance. During the process of image capturing, if the imaging plane of the camera is not fronto-parallel to the surface of an object in reality, the repetitive patterns on the object's surface in real world will appear with a scale shift in the image domain. Thus, the corresponding potential non-local redundancy cannot be removed through the direct utilization of intra block copy and its variants. To address this problem, we begin with a theoretic analysis into the perspective transformation matrix and derive the underlying geometric information that causes the scale shift in the image domain; afterwards, we propose an intra coding framework based on the geometric information to alleviate the scale shift. It is essential for the coding framework to realize block matching in the rectified domain. Our framework mainly consists of two components, i.e., the intra block copy in the rectified domain through planar perspective transformation to obtain a more accurate prediction for the current coding block and the non-local post-processing filtering in the rectified domain after decompression to achieve a better quality of the reconstructed image. The experimental results show that our proposed method can achieve as high as 18% and an average of 5.0% bit-rate saving on the common dataset Urban100 compared to the HEVC reference software HM-SCC-extension with intra block copy and non-local post-processing filtering.
Inverse Reinforcement Learning Application for Discrete and Continuous Environments

We show the application of inverse reinforcement learning (IRL) in discrete and continuous environments based on the apprenticeship focus. The objective is to learn a mathematical definition of a task based on trajectories made by an expert agent. To achieve this, there must be features functions that in some way describe abilities that the agent can learn. Therefore, the description of a task can be formulated as a linear combination of those functions. This learning method was applied in Open-AI gym environments to show the learning process of a task using demonstrations.
Devices and Application Tools for Activity Recognition: Sensor Deployment and Primary Concerns

Sensory modality is a primary concern in sensor-based activity recognition research. The usage of wearable devices and utilizing embedded smartphone sensor data to recognize daily activities has become famous in this research field nowadays. This chapter deals with the challenges of choosing an appropriate sensing device and application tools for data collection. Sensing devices used in previous activity recognition research works have been described in detail with their hardware and software specification. Finally, the description and parameters of some important sensors (accelerometer, gyroscope, etc.) have been given.
From Manuscripts to Programming Languages: An Archivist Perspective

This paper presents the archival treatment of documentation produced by IFIP Working Group 2.1, Algorithmic Languages and Calculi as kept by former chairman Willem van der Poel (1962-69) and Chris Cheney. It highlights the importance of archival treatment based on standards such as International Standard for Archival Description—ISAD(G) and the International Standard Archival Authority Record (Corporate Bodies, Persons and Families)—ISAAR-(CPF) both issued by the International Council on Archives, the Portuguese Guidelines for Archival Description ODA and Encoded Archival Description EAD. The archived collection enables dissemination and effective access to the information for research and in-depth knowledge of computer history and specifically programming languages and formal methods. The paper also addresses the issues of the long-term preservation of archival records produced today in their various formats and the importance of contributing to preserving collective memory and enriching the knowledge about the human society.
Fuzzy rule based SLA generation algorithms for web based multi party negotiation systems

Service Level Agreement (SLA) is an agreement between the service provider and consumer to provide the verifiable quality of services. Using the valuable metrics in SLA, a service consumer could easily evaluate the service provider. Though there are different types of SLA models are available between the consumer and provider, the proposed approach describes the Fuzzy rule base SLA agreement generation among multiple service providers. A negotiation system is designed in this work to collect the different sets of provider services. With their desired quality metrics, a common Fuzzy based SLA report is generated and compared against the existing consumer requirements. From the analysis of the common agreement report, consumers can easily evaluate the best service with the desired Impact service, cost and Quality. The main advantage of this approach is that it reduces the time consumption of a consumer. Moreover, the best service provider can be selected among multiple providers with the desired QoS parameters. At the same time, the bilateral negotiation is enhanced with the approach of multilateral negotiation to improve the searching time of consumers.
Using Local, Contextual, and Deep Convolutional Neural Network Features in Image Registration

Image registration is a well-known problem that arises in many applications in the fields of computer vision, remote sensing, and medical imaging. Many registration methods have been proposed in the literature. However, no single method works well in all kinds of images. In this work, local features and context-based augmented features are used in order to improve the accuracy of the image registration. Furthermore, an attempt has been made to use deep convolutional neural network features on top of those features for further improvement. The paper presents comparative results on image registration with and without feature augmentation and the deep convolutional neural network features. The results from the methods on a widely used benchmark dataset from the University of Oxford confirm improvement in the accuracy of image registration when local and augmented features are used.
Hierarchical Agglomerative Based Iterative Fuzzy Clustering to Impute Missing Values in Health Datasets

As most of the real-world datasets contain missing values (MVs) and prediction accuracy is influenced by them, they need to be handled. The popular approaches to deal with MVs are imputation methods. In this paper, a new imputation approach called Agglomerative based Iterative Fuzzy Clustering is proposed. This method imputes the MVs by systematic calculations of fuzzy c-means clusters. The initial seed points for fuzzy c-means clusters are chosen from Agglomerative clusters and number of fuzzy clusters is decided by Fuzzy Silhouette Index. The accuracy of the proposed method with other widely used cluster-based imputation methods on health data with different percentage of missing entries is estimated in terms of Root Means Square error. The experiment results show that the proposed method provides better performance than other cluster-based imputation methods.
A New Ensemble Technique for Recognize the Long and Shortest Text

In data mining, shorter-text analysis is performed more widely for many applications. Based on the syntax of the language, it is very difficult to analyze the short text with several traditional tools of natural language processing, and this is not applied correctly either. In short text, it is known that there are rare and insufficient data available with this text, and it is difficult to identify semantic knowledge. With the great noise and ambiguity of short texts, it is very difficult to find semantic knowledge. In this paper, it was proposed to replace the coefficient of similarity of cosine with the measure of similarity of Jaro–Winkler to obtain the coincidence of similarity between pairs of text (source text and target text). Jaro–Winkler does a much better job of determining the similarity of the strings because it takes order into account when using positional indices to estimate relevance. It is presumed that the performance of CACT driven by Jaro–Winkler with respect to one-to-many data links offers optimized performance compared to the operation of CACT driven by cosine. An evaluation of our proposed concept is sufficient as validation.
Future of Forensics : An analysation of the efficiency of active learning technology assisted review, a multiple case

The analysis of active learning is an important theme in technology assisted review research. The purpose of the study is to create a measurement to determine the efficiency of active learning algo ...
Applications: Granular Models, Granular Classifiers and Fuzzy Cognitive Maps

In this chapter, we introduce concepts and discuss algorithmic developments of several key categories of models which dwell on ideas of fuzzy sets and information granules: granular fuzzy models, granular classifiers and fuzzy cognitive maps. It is shown that granular results convey more information about the quality of results.
Future Education within Disruptive Technologies Developments

Abstract Our contemporary world is on the verge of crucial changes of an unparalleled pace. The ‘technological changeover’ is the new paradigm caused by the unprecedented evolution of the disruptive technologies. The present world has the tendency to evolve at least exponential, therefore future educational environment is fairly different than its present layout. An entire array of nowadays studies widely recognizes that the progress of the disruptive technologies will pose a meaningful impact over the educational system evolution. Among the most spectacular technologies with disruptive features we should encounter Artificial Intelligence, Blockchain Technology, Cloud Computing, and the like. In an era of technological disruption the education is seen as the new currency. With the help of Artificial Intelligence, for instance, the education system could track how people learn from kindergarten to retirement. Besides, the technology domain will move the centre of gravity from the institutional area to that of the education’s beneficiaries, as we might expect that they will recruit and employ the needed teacher staff, not the institutions. Moreover, the education’s recipients will be the main creators of tomorrow’s professions and within their community the overarching events will happen and the main decisions will be taken in the educational domain.
Calculation of the Depth of Field in Digital Camera Using Fuzzy Inference Systems

This paper presents the development of Fuzzy Inference Systems of Mamdani and Sugeno to calculate the depth of field in a digital camera. A method is proposed to find the minimum distance from the object to the camera where the image has the greatest depth of field, which is determined by the focal length and the diaphragm aperture. To do this, membership functions for these three variables are designated. The linguistic rules in the form of IF-THEN to correlate the inputs (focal length and diaphragm aperture) with the output (minimal distance) are shown. The results of the Mamdani and Sugeno Fuzzy Inference Systems with graphs and contour surfaces are presented, and they were analyzed and compared with theoretical level curves. The proposed method avoids the use of complex algorithms that require a large memory capacity and extensive processing in a common digital camera. The Matlab® Fuzzy Logic Toolbox was used for this work.
Web Authorization Protocols

The proliferation of the use of websites and their counterparts for mobile devices has created a number of problems that did not occur in the past. Typically, one needs to create many and different accounts for all websites and apps she wants to use. However, this means one has to do a lot of booking work. Fortunately, authentication and authorization protocols can be used to solve this and other related problems (e.g., what kind of data can be shared between apps and sites and what kind or data can be made publicly available to anybody). However, even this is not enough so one has to use additional tools to be absolutely sure data are exchanged in a safe way. All these are documented here.
Constitutional Pandemic Surveillance

Non-law enforcement governmental information gathering – special needs searches in the Fourth Amendment parlance – must be “reasonable” to pass constitutional scrutiny. We review the case law on special needs searches to shed light on the contours of this reasonableness inquiry. Then, to aid in this analysis, we measure the perceived intrusiveness of pandemic surveillance through two nationally representative surveys of Americans conducted in the Spring of 2020, during the COVID-19 crisis. Our results show that, even at the height of an international pandemic, people find surveillance for public health purposes to be more intrusive than surveillance for traditional law enforcement purposes. To account for these strong privacy concerns, we propose safeguards that we believe would make cell phone location tracking and other similar digital monitoring regimes constitutionally reasonable.