Reviewerly/exHarmony huggingface dataset:
Combining Network Analysis with Structural Matching for Design Pattern Detection

Context: Design pattern detection is a very important research on software reuse, which can greatly help software maintenance and reconstruction. At present, many researchers have invested in this work and proposed a variety of methods for detection. Method: This paper extends the graph matching technology based on the past and proposes a new method that combines network analysis and structural matching for detection. First, we use network level analysis to obtain important nodes, then use the neighborhood path matching algorithm to match the pattern instance. Result: We describe the detection of five patterns on four open source systems, then analyze and compare with the other three methods, for achieving high precision and recall, which demonstrates that our method is effective. Conclusion: Using combining network analysis with structural matching can well detect these pattern instances, and these instances are also especially important for future software refactoring.
Efficient scheme for compressing and transferring data in hadoop clusters

The size of data collected by public institutions and industries is rapidly exploding. As the data that needs to be processed grows larger, there is a limit to processing big data simply by using scale-up servers. To address this limitation, distributed cluster computing systems that use scale-out servers have emerged. However, if the network bandwidth is not used efficiently, the distributed cluster computing systems can not maximize the performance of the scale-out servers. In this paper, we propose an efficient scheme for compressing and transferring data in Hadoop clusters. The proposed method selects an appropriate compression algorithm by calculating the data transfer cost model based on the information entropy of data and network bandwidth. Experimental results show that the data transfer time and the amount of data transfer between the data nodes of the proposed scheme are significantly reduced.
On the Impact of Rooftop Solar PV on Local Temperatures in Urban Areas

Globally, the building sector accounts for around 36% of the final energy consumption and it produces 40% of CO2 emissions[1]. Similarly in Morocco, this sector is the largest energy consumer reaching a threshold of 33%, 26% for residential buildings and 7% for services (hotels, office buildings, schools, universities and hospitals). Thus, building consumption exceeds the industry sector (21%) and agriculture (8%) [2]. Such patterns are expected to increase quickly in the coming years. Integrating solar Photovoltaic (PV) panels in urban environments is an efficient solution to reduce electricity bills and CO2 emissions of buildings. This paper proposes a methodology to investigate the impact of large-scale deployment of solar PV on the urban micro-climate. This methodology consists of using the building footprints, building materials and meteorological conditions to calculate the urban surface temperatures before and after the integration of PV panels with the aim to assess any PV related Heat Islands (PVHI).
Optimizing Reverse Image Search by Generating and Assigning Suitable Captions to Images

Reverse image search is a content-based image retrieval (CBIR) query technique that takes a sample image as an input, and search is performed based on it. Reverse image search is characterized by a lack of search terms. Using reverse image search, one can find the original source of images, find plagiarized photos, detect fake accounts on social media, etc. Reverse image search works by uploading an image by the user, and searching of images is carried out by using the corresponding meta tags, HTML tags or color distributions of the image. The search engine currently functions by using this information and not the context of the image, thus resulting in inaccurate outcomes. Labeling the image using keywords relevant to the image is a very generic explanation to an image and can lead to ambiguous interpretations. This can be overcome by giving a more specific description to an image, thereby preventing misinterpretations and providing more specific results. The proposed system aims at optimizing the search results by depicting more clearly the relationships between the objects in an image with the help of image captioning.
Actionable Software Metrics

Background: Practitioners would like to take action based on software metrics, as long as they find them reliable. Existing literature explores how metrics can be made reliable, but remains unclear if there are other conditions necessary for a metric to be actionable. Context & Method: In the context of a European H2020 Project, we conducted a multiple case study to study metrics' use in four companies, and identified instances where these metrics influenced actions. We used an online questionnaire to enquire about the project participants' views on actionable metrics. Next, we invited one participant from each company to elaborate on the identified metrics' use for taking actions and the questionnaire responses (N=17). Result: We learned that a metric that is practical, contextual, and exhibits high data quality characteristics is actionable. Even a non-actionable metric can be useful, but an actionable metric mostly requires interpretation. However, the more these metrics are simple and reflect the software development context accurately, the less interpretation required to infer actionable information from the metric. Company size and project characteristics can also influence the type of metric that can be actionable. Conclusion: This exploration of industry's views on actionable metrics help characterize actionable metrics in practical terms. This awareness of what characteristics constitute an actionable metric can facilitate their definition and development right from the start of a software metrics program.
On the resistance to Byzantine and unresponsive servers in code-based PIR schemes

In private information retrieval (PIR) from coded storage servers, consider the case where some of servers are Byzantine adversaries and unresponsive. There have been proposed several specialized schemes guaranteeing that the user can correctly obtain the desired message even in the adversarial situation. However, to the best of our knowledge, such resistance to the adversaries in PIR schemes based on arbitrary codes have been not precisely characterized. In this paper, we reveal that the exact resistance to Byzantine and unresponsive servers is expressed in terms of the coset distance of linear codes in linear PIR schemes based on arbitrary storage code.
A Zero watermarking Approach for Biometric Image Security

Unauthorized entry in the database is turning very prevalent in digital felony. The obligatory and foremost priority is to shield the data when it is dispersed in a cloud server. The incentive of this work Commences with emphasizing the prevention of outlawed access to characteristics of data practiced by cybercriminals. Favor to adopt biometrics becomes one of the trending subjects when it comes to preventing illegitimate access over digital media because of its unique features. This paper preface the concept of zero-bit watermarking in the digital palm-print image. Many work-related to iris and fingerprint have already done and still alarming concerns are rising. Paramount priority of accepting modern data set confers us to figure out novel and rich features of palm-print which constraints on unlicensed access. The Digital pattern is obtained from unique features of the palm-print image without causing any disfigurement on it. This work utilizes SVD and DWT for the realization of zero watermarking since this method is very substantial contrary to diverse image processing menace. As the attributes of palm-print are unique, so the duplicity of data is eradicated by this proposed method.
Virtual Reality: A Pedagogical model for Simulation Based Learning

Virtual Reality (VR) technologies are quick growing and recognized within the education and coaching. This paper describes the perspective of the necessary technologies on which virtual reality environments depend on and bridges the gaps between the existing papers on student perceptions about VR technology and its potential pedagogical model. This study uses a mixed-methods approach, which integrated qualitative and quantitative elements using questionnaires with closed and open. The results were statistically analysed using the Fisher exact and Kruskal-Wallis tests. The qualitative statements were contextualized with the more relevant points of view identified to coincide with aspects of VR found in the literature. The results offered a clear summary of the benefits and possible pitfalls of using VR in particular, with a more specific focus in the field of learning. It was observed that the main challenges in adopting VR use in education were the expense of acquiring VR equipment, potential health issues such as motion sickness and learners struggling to adapt to use of VR.
A Secure End-to-End Key Exchange Mechanism by Cooperation of Multiple Devices Using QR Codes

End-to-end key exchange for the subsequent secret sharing and secure communication between two remote parties has been an important issue due to the threats of eavesdropping and Man-In-The-Middle (MITM) attacks. In this paper, we propose a secure end-to-end key exchange mechanism between two remote parties by cooperation of multiple devices at each party using QR (Quick Response) codes. In the key exchange process, the data transmission will be conducted by two different applications via two different infrastructure networks, SMS (Short Message Service) via cellular network (e.g. LTE, 4G, etc) and Email via Ethernet respectively, between the two remote parties using two different devices at each party in order to mitigate security risks. Public-key cryptography will be adopted for the data transmission during the key exchange and the corresponding asymmetric key pair will be used only once. The data transmission within the multiple devices at each party only uses QR codes (scan and display) without involving any network based communication. The main contribution of this paper is that a novel secure end-to-end key exchange approach has been proposed in which unless both the devices using cellular network and Ethernet have been compromised or MITM attacked by the same attacker the key will not be leaked during the exchange process. We verified the main features of the proposed mechanism and confirmed the effectiveness of the design.
Disinformation, Digital Information Equality, and Electoral Integrity

Electoral disinformation campaigns intentionally deceive voters, thereby disrupting the notion of fair elections and challenging western democracies to craft new policies that safeguard their electoral integrity. Over-regulation of political speech, however, can imperil the political participation of the informed voter, who in turn engages and influences other voters. Such policies for regulating electoral disinformation must therefore balance the tension between curbing speech and encouraging voters to engage in political participation through the free flow of information. Canadian elections law offers a possible solution for jurisdictions seeking to effectively regulate disinformation without unduly stifling free expression through the principle of informational equality. We argue for an updated principle of digital information equality to address the harms of disinformation. By reinvigorating the principle of information equality and adapting it from a theoretical concept to a regulatory device, this article proposes a new method to regulate electoral disinformation while supporting an informed electorate, respecting democratic principles, and protecting electoral integrity. In so doing, the article identifies three harmful examples of electoral disinformation that warrant increased regulation and concludes with recommendations for other jurisdictions seeking to regulate disinformation in the electoral context.
MSE-Optimal Neural Network Initialization via Layer Fusion

Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. However, the use of stochastic gradient descent combined with the nonconvexity of the underlying optimization problems renders parameter learning susceptible to initialization. To address this issue, a variety of methods that rely on random parameter initialization or knowledge distillation have been proposed in the past. In this paper, we propose FuseInit, a novel method to initialize shallower networks by fusing neighboring layers of deeper networks that are trained with random initialization. We develop theoretical results and efficient algorithms for mean-square error (MSE)- optimal fusion of neighboring dense-dense, convolutional-dense, and convolutional-convolutional layers. We show experiments for a range of classification and regression datasets, which suggest that deeper neural networks are less sensitive to initialization and shallower networks can perform better (sometimes as well as their deeper counterparts) if initialized with FuseInit.
Convolutional Network for Generic Object Contour Detection with Stereo Vision

Object contour detection is the task of extracting the shape created by the boundaries between objects in an image. The conventional method previously limits the detection targets to specific categories, or detects edges of patterns inside an object. We propose a new method to represent a contour image with a distance to the boundary as a pixel value. We also propose a deep convolutional network for generic object contour detection combined with stereo vision. In the network learning, false positives inside objects are penalized with increasing distance to boundaries. As a result of experiments, the proposed network drew a good precision-recall curve, and F-measure was about 0.7 for Driving dataset. False detections inside the object were significantly reduced. Furthermore, using common network structure and similar inference targets, we propose a network that simultaneously estimates disparity and contour with fully shared weights. This network reduced the amount of calculation and memory capacity by half, and the drop in accuracy was slight.
Research on Copyright Protection of Digital Publishing in the Era of Big Data

With the development of network information technology, the era of big data brings new opportunities for digital publishing industry. Big data publishing has become one of the trends of future publishing, and has become the core competitiveness of publishing industry, and also brings new challenges for digital publishing. Due to the increasing popularity of data sharing in the era of big data, the problem of network infringement has also been gradually paid attention to, so the research on copyright protection is very important. Based on the characteristics and current situation of the development of digital publishing in the big data environment, this paper analyzes in detail the main problems and difficulties of copyright protection in the big data era. On this basis, this paper puts forward the era of big data is copyright protection of digital publishing strategy, in order to further improve the construction of copyright protection of digital publishing system.
Detecting Offensive Text on Facebook Using Natural Language Processing and Machine Learning

All the content posted on Facebook may or may not be appropriate for the viewers. A part of this content, which is inappropriate, can have a negative effect on viewer’s psychology, especially if the content is meant for the viewer. Facebook’s existing process of manual filtering is quite time-consuming and may require a huge workforce to check every content posted by millions of Facebook users. In addition, the current manual filtering process carried out looks only for the presence of any abusive word. This process does not filter out sentences, which do not contain any abusive word but can be perceived as offensive by the viewer. This project aims to address the said issues in manual filtering by automating the process of offensive and profane text content filtering. A combination of lexical and semantic models is used for filtering Facebook textual content with the help of machine learning and natural language processing. The lexical approach uses a dictionary-based method to identify the presence of any profane and offensive words in a comment or post data, whereas semantic approach analyzes the context, i.e., the meaning of the sentence. After determining the presence of any inappropriate content with respect to word or context, the manual system hides the corresponding sentence, thereby restricting the viewer from being exposed to offensive content. The proposed method exhibits good accuracy for classifier considering that it is not designed to detect offensive content on the ground racism and nationalism.
Adaptive Algorithm For Tracking Maneuvering Targets In A Complex Jamming Environment For A Radar With Range Rate Measurement

To increase the detection efficiency of moving targets masked by interfering reflections, radars are widely used, in which target spatial coordinates and also range rate are measured. When solving the radar data processing problems, the presence of target range rate measurements increases the target tracking efficiency in the presence of false plots, and can significantly improve the tracking quality, especially with intensive target maneuvering. In the work on the mathematical apparatus basis of the mixed Markov processes in discrete time, a quasi-optimal adaptive algorithm for tracking maneuvering targets in a difficult jamming environment for a multifunctional radar with range rate measurement is developed. Using statistical computer modeling, a comparative analysis of tracking algorithms with and without measurement of range rate in the same interference environment at different data rates is performed.
On the Quantum Electrodynamics of Nanophotonic Systems

Problems of quantum electrodynamics of nanoobjects essential for development of new nanophotonic systems are discussed. According to the theory of natural oscillatory systems (NOSs), "interaction" between the objects is interpreted as a quantum-dynamic phenomenon meaning a stable trend arising from the quantum chaos. As an opposite, "interchange" is denominated as the permanent stochastic exchange with action quanta between different NOSs in 4D spacetime, being the physical base of the quantum chaos. The Tetrode-Wheeler-Feynman's concept of "direct interparticle action" is reconciled with both the quantum radiation-absorption and the Coulomb interaction. A conservation law for the action is supposed to be a necessary condition for the momentum-energy conservation. The "classic" conservation law for the momentum-energy is considered as derivative, being valid for the momentum as well as some physical value that is an integral over 3D space from a linear combination of stress-energy tensor principal diagonal terms. Such redefinition enables the unconditional quantization of the energy unlike "orthodox" quantum theory.
The Technique of Implementation Security into Clinical Internet of Things

An approach for implementation Trust, Identity, Privacy, Protection, Safety, Security into Clinical Internet of Things was proposed. Frequency domain and time domain interoperability with itself for gain safety of information maintenance is considered. The technique uses the coding scheme of the so-called Regular Pulse Excitation Long Term Prediction Linear Predictive Coder for electrocardiograms processing. The proposed approach illustrates a linkage between direct attacker motivations and implied motivations. Each direct motivation, or technical objective, is mapped to one or more implied motivations, or business impact that differs it's from the existing ones. The Spearman correlation coefficient 0.86 has been achieved on examination.
Secure NoSQL Over Cloud Using Data Decomposition and Queryable Encryption

NoSQL-based databases are attractive to store and manage big data, mainly due to high scalability and data modeling flexibility. However, security in NoSQL-based databases is not as compared to the SQL-based relational databases, which raises concerns for users. Specifically, the security of data-at-rest is of serious concern for the users, who deploy their databases on the cloud, because any unauthorized access to the servers will expose the data easily. There have been some efforts to enable encryption for data at rest for NoSQL-based databases. However, most of the existing solutions do not support secure query processing and are difficult to integrate with the applications. In our work, we address the NoSQL data-at-rest security issue by proposing a system which decomposes a given database into two sub-databases. Then encrypt the data, support secure query processing, and enable seamless integration with NoSQL-based databases. The proposed solution for the data at rest is based on a combination of Chaotic Encryption (CE), Order Preserving Encryption (OPE), and Advanced Encryption Standard (AES). The database decomposition into two sub-databases helps to improve data confidentiality while providing comparable performance to the state-of-the-art baseline method.
Acceleration of Polynomial Matrix Multiplication on Zynq-7000 System-on-Chip

In this paper, we propose a second-generation hardware solution which boasts more versatility, efficiency and scalability compared to our previous design. This is achieved through the design of a highly versatile PMM accelerator which supports polynomial matrices of any size, as a component of the embedded system developed within the Xilinx Zynq-7000 AP SoC. Experimental results demonstrate the efficiency and effectiveness of our novel SoC-based PMM accelerator in the context of a generic problem, where a maximum speed-up of ≈ 67× is accomplished, without compromising the accuracy.
IndoorNet: Generating Indoor Layouts from a Single Panorama Image

IndoorNet is an approach to generate 3D layouts for indoor scenes. By employing a fully convolutional network (FCN), it predicts the layout of the room using only a single 360 degree panorama image. The FCN uses geometrical features, i.e., vanishing points along with the panoramic image for better contextual understanding. Further, we optimize the boundary map and the corner map to improve their accuracy. We then use these improved maps to generate the 3D layout of the room. As compared to the state of the art, our network architecture is faster and lighter, with improved results.
A Multi-level and Multi-label Annotation Strategy for User Questions in ICT Customer Service

In the field of customer service, speech and text records generated from users often contain a wealth of product information. Identifying users' intent, mining hot issues, and analyzing relationship of users' needs from massive data are basic tasks to improve the intelligent level of customer service operation and maintenance, while an annotated question corpus is prerequisite for training machines to understand information needs of users. Taking the offline bidding tool service item in the E-commerce platform of the State Grid ICT system as an example, compared to the annotation with one single label, this paper develops a multi-level and multi-label question category annotation strategy based on the ICT system function module, and forms a corresponding annotated corpus. Using the schedule, 700 customer service speech records about the offline bidding tool were annotated with a total number of 911 questions, covering 68 question types. The annotation has obtained appropriate inter-annotator agreement to ensure corpus quality. Furthermore, the distribution and relationship of the annotated labels are measured by descriptive statistics and social network map.