Reviewerly/exHarmony huggingface dataset:
Cloud-Based Outsourcing for Enabling Privacy-Preserving Large-Scale Non-Negative Matrix Factorization

It is inevitable and evident that outsourcing complicated intensive tasks to public cloud vendors would be the primary option for resource-constrained clients in order to save cost. Unfortunately, the public cloud vendors are usually untrusted. They may inadvertently leak the data or misuse the user’s data, compromise user’s privacy or intentionally corrupt computational results to make the system unreliable. It is therefore important how to stop this happening whilst embracing the computational power of public cloud vendors. Non-negative matrix factorization (NMF) is a significant method for conducting data dimension reduction, which has been widely used in large-scale data processing. Nevertheless, due to its non-polynomial hardness, NMF cannot be conducted efficiently using local computation resources, especially when dealing with big data. Motivated by this issue, we address this by presenting a novel outsourced scheme for NMF (O-NMF), which aims to lessen clients’ computing burden and tackle secure problems faced by outsourcing NMF. Particularly, based on two non-collusion servers, O-NMF exploits Paillier homomorphism to preserve data privacy. Additionally, O-NMF allows a verification mechanism to assist clients in verifying returned results with high probability. Security analysis and experimental evaluation demonstrates that the validity and practicality of O-NMF is also provided in this work.
Fast and robust distributed subgraph enumeration

We study the subgraph enumeration problem under distributed settings. Existing solutions either suffer from severe memory crisis or rely on large indexes, which makes them impractical for very large graphs. Most of them follow a synchronous model where the performance is often bottlenecked by the machine with the worst performance. Motivated by this, in this paper, we propose RADS, a Robust Asynchronous Distributed Subgraph enumeration system. RADS first identifies results that can be found using single-machine algorithms. This strategy not only improves the overall performance but also reduces network communication and memory cost. Moreover, RADS employs a novel region-grouped multi-round expand verify &amp; filter framework which does not need to shuffle and exchange the intermediate results, nor does it need to replicate a large part of the data graph in each machine. This feature not only reduces network communication cost and memory usage, but also allows us to adopt simple strategies for memory control and load balancing, making it more robust. Several optimization strategies are also used in RADS to further improve the performance. Our experiments verified the superiority of RADS to state-of-the-art subgraph enumeration approaches.
A text semantic topic discovery method based on the conditional co-occurrence degree

The topic discovery method, as an effective tool for semantic mining and a key means to extract new features from original text, plays an important role in the field of text mining and knowledge discovery. To solve the problems encountered in traditional topic models, such as the loss of semantic information and the ambiguity of topic concepts, as well as the crossover and coverage among topics, we propose a semantic topic discovery method based on the conditional co-occurrence degree (CCOD_STDM). First, every document is split into multiple subdocuments according to the semantic structure of the document and the independence decision rules. Second, combinatorial words with strong semantic relevance are extracted based on the conditional co-occurrence degree within the subdocuments. Based on these combinatorial words, new subdocuments are formed by feature expansion and content reconstruction. Third, “topic-word” distributions and “document-topic” distributions of new subdocuments are obtained by topic modeling with Gibbs sampling. Finally, “document-topic” distributions of the original documents are obtained by merging new subdocuments’ “document-topic” distributions with specific strategies. The numerical experiments are compared with six topic models and two evaluation methods on seven kinds of public corpora, and the experimental results verify the superiority of CCOD_STDM and its efficiency in topic discovery. More importantly, a case study illustrates that the combinatorial words can effectively avoid the polysemy problem and can facilitate the condensation and summary of topics.
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird'seye-view images contain sharper features (e.g, road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford Robot-Car Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.
C.45 Classification Rules Model for Determining Students Level of Understanding of the Subject

Abstract This study aims to classify the level of understanding of students at STIKOM Tunas Bangsa. Data obtained from the results of even semester semester student questionnaires, with data samples of 165 students. The attributes used are 5, namely communication (C1), learning atmosphere (C2), learning media (C3), appearance (C4) and teaching methods (C5). The method used in this research is C4.5 Algorithm and processed using RapidMiner software to make decision trees. From the results of the study obtained eight rules to determine the level of student understanding, 4 rules understand and 4 rules do not understand. The C4.5 algorithm is accurately applied to determine the level of understanding of students with an accuracy rate of 87.50%. With this analysis, it is expected that it can help institutions in improving the quality of education through the performance of lecturers in the teaching and learning process. So as to produce STIKOM Tunas Bangsa graduates who are rich in knowledge and useful for the community.
Managing a pool of rules for credit card fraud detection by a Game Theory based approach

In the automatic credit card transaction classification there are two phases: in the Real-Time (RT) phase the system decides quickly, based on the bare transaction information, whether to authorize the transaction; in the subsequent Near-Real-Time (NRT) phase, the system enacts a slower ex-post evaluation, based on a larger information context. The classification rules in the NRT phase trigger alerts on suspicious transactions, which are transferred to human investigators for final assessment. The management criteria used to select the rules, to be kept operational in the NRT pool, are traditionally based mostly on the performance of individual rules, considered in isolation; this approach disregards the non-additivity of the rules (aggregating rules with high individual precision does not necessarily make a high-precision pool). In this work, we propose to apply, to the rule selection for the NRT phase, an approach which assigns a normalized score to the individual rule, quantifying the rule influence on the overall performance of the pool. As a score we propose to use a power-index developed within Coalitional Game Theory, the Shapley Value (SV), summarizing the performance in collaboration. Such score has two main applications: (1) it can be used, within the periodic rule assessment process, to support the decision of whether to keep or drop the rule from the pool; (2) it can be used to select the k top-ranked rules, so as to work with a more compact rule set. Using real-world credit card fraud data containing approximately 300 rules and 3×105 transactions records, we show that: (1) this score fares better – in granting the performance of the pool – than the one assessing the rules in isolation; (2) that the same performance of the whole pool can be achieved keeping only one tenth of the rules — the top-k SV-ranked rules. We observe that the latter application can be reframed in terms of Feature Selection (FS) task for a classifier: we show that our approach is comparable w.r.t benchmark FS algorithms, but argue that it presents an advantage for the management, consisting in the assignment of a normalized score to the individual rule. This is not the case for most FS algorithms, which only focus in yielding a high-performance feature-set solution.
A Survey On Cloud Security Issues And Blockchain

The need for cloud computing is gradually increasing day by day. The cloud computing security is the major difficulty. Since the data in the cloud has to be transferred through internet, the security of data becomes a major concern. The key mechanisms for data protections like integrity, accountability, privacy, access control, authentication, authorization must be maintained. Blockchain is a technology which makes cloud computing better. Blockchain overcomes the security issues in cloud computing. This survey aims at analyzing and comparing various issues in the cloud environment and security issues using blockchain.
Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm

Abstract The importance of efficiency in the space of search rules C4.5 decision tree algorithm has been the focus of a lot of researchers. Therefore, the development needs to be conducted to form a new, more efficient method but it can not be separated from the accuracy of the analysis as the results of the algorithm itself. For that purpose, by using a genetic algorithm (GA), it is expected to optimize and simplify the search rules of more complex combinations. The use of C4.5 with Hybrid genetic algorithm in search of a more effective rules requires a better understanding and a long time. But the use of the two algorithms will be mostly effective if the cases faced are very complex, having more branching condition and highly accurate.
An End-to-End Deep Learning Image Compression Framework Based on Semantic Analysis

Lossy image compression can reduce the bandwidth required for image transmission in a network and the storage space of a device, which is of great value in improving network efficiency. With the rapid development of deep learning theory, neural networks have achieved great success in image processing. In this paper, inspired by the diverse extent of attention in human eyes to each region of the image, we propose an image compression framework based on semantic analysis, which creatively combines the application of deep learning in the field of image classification and image compression. We first use a convolutional neural network (CNN) to semantically analyze the image, obtain the semantic importance map, and propose a compression bit allocation algorithm to allow the recurrent neural network (RNN)-based compression network to hierarchically compress the image according to the semantic importance map. Experimental results validate that the proposed compression framework has better visual quality compared with other methods at the same compression ratio.
A serious game for teaching the fundamentals of ISO/IEC/IEEE 29148 systems and software engineering – Lifecycle processes – Requirements engineering at undergraduate level

Abstract In the context of software engineering education, there is a recurrent demand for new approaches and techniques that support the application and transfer of knowledge to real-life situations with the aim of encouraging a more active learning among students. In particular, serious games have recently become an important learning resource for teaching the fundamentals of software process standards at undergraduate level. However, poor effort has been made to create a serious game that supports the teaching of the ISO/IEC/IEEE 29148:2011 Systems and Software Engineering – Lifecycle Processes – Requirements Engineering, an international standard that specifies the required processes that are to be implemented by requirements engineering for systems and software products (including services) throughout the lifecycle. With this in mind, a serious game called “Requengin” has been developed to provide undergraduate students with an interactive learning environment to facilitate the introduction of ISO/IEC/IEEE 29148:2011. The main objective of the game is to strengthen the comprehension and application of the main processes of the standard and some related requirements engineering techniques. Requengin was designed to simulate an academic library where players must apply the requirements engineering processes with the aim of changing the traditional management system by a software system while they receive, at the same time, preliminary training in ISO/IEC/IEEE 29148:2011. The results obtained by empirical evaluation indicate that Requengin could potentially contribute to an improvement in students’ acquisition of knowledge about ISO/IEC/IEEE 29148:2011, while also improving levels of motivation.
DeepLink: Recovering issue-commit links based on deep learning

The links between issues in an issue-tracking system and commits resolving the issues in a version control system are important for a variety of software engineering tasks (e.g., bug prediction, bug localization and feature location). However, only a small portion of such links are established by manually including issue identifiers in commit logs, leaving a large portion of them lost in the evolution history. To recover issue-commit links, heuristic-based and learning-based techniques leverage the metadata and text/code similarity in issues and commits; however, they fail to capture the embedded semantics in issues and commits and the hidden semantic correlations between issues and commits. As a result, this semantic gap inhibits the accuracy of link recovery. To bridge this gap, we propose a semantically-enhanced link recovery approach, named DeepLink, which is built on top of deep learning techniques. Specifically, we develop a neural network architecture, using word embedding and recurrent neural network, to learn the semantic representation of natural language descriptions and code in issues and commits as well as the semantic correlation between issues and commits. In experiments, to quantify the prevalence of missing issue-commit links, we analyzed 1078 highly-starred GitHub Java projects (i.e., 583,795 closed issues) and found that only 42.2% of issues were linked to corresponding commits. To evaluate the effectiveness of DeepLink, we compared DeepLink with a state-of-the-art link recovery approach FRLink using ten GitHub Java projects and demonstrated that DeepLink can outperform FRLink in terms of F-measure.
Knocker

While smartphones have enriched our lives with diverse applications and functionalities, the user experience still often involves manual cumbersome inputs. To purchase a bottle of water for instance, a user must locate an e-commerce app, type the keyword for a search, select the right item from the list, and finally place an order. This process could be greatly simplified if the smartphone identifies the object of interest and automatically executes the user preferred actions for the object. We present Knocker that identifies the object when a user simply knocks on an object with a smartphone. The basic principle of Knocker is leveraging a unique set of responses generated from the knock. Knocker takes a multimodal sensing approach that utilizes microphones, accelerometers, and gyroscopes to capture the knock responses, and exploits machine learning to accurately identify objects. We also present 15 applications enabled by Knocker that showcase the novel interaction method between users and objects. Knocker uses only the built-in smartphone sensors and thus is fully deployable without specialized hardware or tags on either the objects or the smartphone. Our experiments with 23 objects show that Knocker achieves an accuracy of 98% in a controlled lab and 83% in the wild.
A comprehensive albedo model for solar energy applications: Geometric spectral albedo

Albedo is the measure of irradiation energy, which comes from reflecting surroundings. Location, time, geometry, and weather conditions influence the value of albedo. By altering any of the aforementioned parameters, even a little, the albedo value changes. This makes albedo challenging to model. Here we present a mathematical model to cluster all the influential parameters, which affect albedo. We show how complexity of albedo can be expressed as function of reflected spectrum of shadows geometry in a 3-dimensional environment. Performance of the suggested model is examined through real-time measurements and compared with other models. Absolute deviation between modelled and measured average diurnal albedos for a geometrically complex location containing several materials was less than 0.31%. The model also proves that the albedo of a surface is always lower than or equal to its reflectivity. The model can be applied in computer simulation to remotely calculate the value of albedo without the need of in-situ measurements. We show how the proposed model can be used in many fields of research related to climate science, computer graphics, geology, agriculture, solar energy and photovoltaics.
Global and local multi-view multi-label learning

In order to process multi-view multi-label data sets, we propose global and local multi-view multi-label learning (GLMVML). This method can exploit global and local label correlations of both the whole data set and each view simultaneously. What’s more, GLMVML introduces a consensus multi-view representation which encodes the complementary information from different views. Related experiments on three multi-view data sets, fourteen multi-label data sets, and one multi-view multi-label data set have validated that (1) GLMVML has a better average AUC and precision and it is superior to the classical multi-view learning methods and multi-label learning methods in statistical; (2) the running time of GLMVML won’t add too much; (3) GLMVML has a good convergence and ability to process multi-view multi-label data sets; (4) since the model of GLMVML consists of both the global label correlations and local label correlations, so parameter values should be moderate rather than too large or too small.
Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid)

Semantic segmentation of videos helps in scene understanding, thereby assisting in other automated video processing techniques like anomaly detection, object detection, event detection, etc. However, there has been limited study on semantic segmentation of videos acquired using Unmanned Aerial Vehicles (UAV), primarily due to the absence of standard dataset. In this paper, a new UAV aerial video dataset (ManipalUAVid) for semantic segmentation is presented. The videos have been acquired in a closed university campus, and fine annotation is provided for four background classes viz. constructions, greeneries, roads, and waterbodies. Also, the performance of four semantic segmentation approaches: Conditional Random Field (CRF), U-Net, Fully Convolutional Network (FCN) and DeepLabV3+ are analysed on ManipalUAVid dataset. It is seen that these algorithms perform competitively on UAV aerial video dataset and achieves an mIoU of 0.86, 0.86, 0.86 and 0.83 respectively.
The Convergence of IoE and Blockchain: Security Challenges

To build a large-scale distributed Internet of Things (IoT), a feasible prototype for Internet of Everything, blockchain can provide strong support with its excellent characteristics such as traceability and openness. Despite that the blockchain technology ideally enhances the reliability and security of IoT systems, emerging new security challenges remain to be resolved. This article details the security vulnerabilities in the convergence of blockchain and IoT as well as corresponding feasible solutions.
Teaching AI agents ethical values using reinforcement learning and policy orchestration

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual-bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.
SoftAuthZ: A Context-Aware, Behavior-Based Authorization Framework for Home IoT

The smart home is one of the most prominent applications in the paradigm of the Internet of Things (IoT). While, it has added a level of comfort and convenience to our everyday life, at the same time, it brings a unique security challenge of mitigating insider threats, posed by legitimate users. Such threats primarily arise due to sharing of IoT devices and the presence of complex social and trust relationships among the users. The state-of-the-art home IoT platforms manage access control by deploying various multifactor authentication mechanisms. Nevertheless, such hard-security measures are inadequate to thwart insider threats, and there is a growing need to integrate user behavior and environmental contexts to make intelligent authorization decisions. In this article, we propose a novel context-sensitive and behavior-based security framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SoftAuthZ</i> , that incorporates soft-security mechanisms, such as belief, confidence, etc., to support authorization decisions. Our framework integrates multiple IoT environment-specific attributes, such as environmental context, nature of the device, requested capabilities (actions), users’ trust levels concerning the home environment, and variability in device access requests into a linear regression model, and computes confidence related to access requests. Such confidence scores can be used by the home IoT platform to make authorization decisions. Extensive analysis and simulation-based performance evaluation validate the efficacy of our framework, demonstrating that it can classify users based on their device usages, and also achieve higher rates of successful authorization.