Reviewerly/exHarmony huggingface dataset:
Meta-heuristic approaches to tackle Skill Based Group allocation of Students in Project Based Learning Courses

In the arena of software engineering, Project Based Learning (PBL) is one of the fundamental components of practical based assessment. PBL involves team formation where necessary skills are needed to execute the project. Traditionally, the teams were randomly allocated based on individual preferences. To cab on this issue, preference based model needs few refinements such as skills needs to be identified by the facilitator while the students provide the necessary skill data. This way, students get assigned based on their skill rather than just random allocation. In a worst case scenario for random allocation, a team can end up with a very strong team having high skills or vice versa where a team has all of its members with limited skill or few skills are missing. The group created by skill preference would allow each group to more or less have the same strength and nearly all skills would be present in a group. In this paper, a method is extended from its original to cater for other state-of-the-art optimization techniques rather than just genetic algorithm to find a method that can suit small or large dataset. The objective function takes into account the differences between the total skill set of each group with the average total skill set needed for each group and the missing skill penalty of each group is added. Missing skill penalty is incurred due to not satisfying all the constraints such as non-presence of all the skills in a group. The skill rating allows better selection of members in a software engineering course. The results discussed in this paper are from 5 courses of one university.
Novel View Synthesis with Multiple 360 Images for Large-Scale 6-DOF Virtual Reality System

We present a novel view synthesis method that allows users to experience a large-scale Six-Degree-of-Freedom (6-DOF) virtual environment. Our main contributions are the construction of a large-scale 6-DOF virtual environment using multiple 360 images as well as synthesis of a scene from novel viewpoints. Novel view synthesis from a single 360 image can give free viewpoint experience with full 6-DOF of head motion to players, but the moveable space is limited within a context of the image. We propose a novel view synthesis process that references multiple 360 images via reconstructing a large-scale real world based virtual data map and perform a weighted blending for interpolating multiple novel view images. Our results show that our approach provides a wider area of virtual environment as well as a smooth transition between each reference 360 images.
Code-Switching Language Modeling with Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English

Code-switching (CS) is a widespread phenomenon among bilingual and multilingual societies. The lack of CS resources hinders the performance of many NLP tasks. In this work, we explore the potential use of bilingual word embeddings for code-switching (CS) language modeling (LM) in the low resource Egyptian Arabic-English language. We evaluate different state-of-the-art bilingual word embeddings approaches that require cross-lingual resources at different levels and propose an innovative but simple approach that jointly learns bilingual word representations without the use of any parallel data, relying only on monolingual and a small amount of CS data. While all representations improve CS LM, ours performs the best and improves perplexity 33.5% relative over the baseline.
Are existing code smells relevant in web games? an empirical study

In software applications, code smells are considered as bad coding practices acquired at the time of development. The presence of such code smells in games may affect the process of game development adversely. Our preliminary study aims at investigating the existence of code smells in the games. To achieve this, we used JavaScript code smells detection tool JSNose against 361 JavaScript web games to find occurrences of JavaScript smells in games. Further, we conducted a manual study to find violations of known game programming patterns in 8 web games to verify the necessity of game-specific code smells detection tool. Our results shows that existing JavaScript code smells detection tool is not sufficient to find game-specific code smells in web games.
DwfwFcm: An effective fuzzy c-means clustering framework considering the different data weights and feature weights

Fuzzy c-means algorithm (Fcm) frequently applid in machine learning has been proven an effective clustering approach. However, the traditional Fcm cannot distinguish the importance of the different data objects and the discriminative ability of the different features in the clustering process. In t his paper, we propose a new kind of Fcm clustering framework: DwfwFcm.Considering the different data weights and feature weights, an adaptive data weights vector and an adaptive feature weights matrix are introduced into the conventional Fcm and a new objective function is constructed. By the proposed objective function, the corresponding scientific updating iterative rules of the membership matrix, the weights of the different feature, the weights of the different data object and the cluster centers can be derived theoretically.Experimental results have demonstrated that the algorithm proposed in this paper can deliver consistently promising results and improve the clustering performance greatly.
Efficient Collision Attack Frameworks for RIPEMD-160

RIPEMD-160 is an ISO/IEC standard and has been applied to generate the Bitcoin address with SHA-256. Due to the complex dual-stream structure, the first collision attack on reduced RIPEMD-160 presented by Liu, Mendel and Wang at Asiacrypt 2017 only reaches 30 steps, having a time complexity of $$2^{70}$$ . Apart from that, several semi-free-start collision attacks have been published for reduced RIPEMD-160 with the start-from-the-middle method. Inspired from such start-from-the middle structures, we propose two novel efficient collision attack frameworks for reduced RIPEMD-160 by making full use of the weakness of its message expansion. Those two frameworks are called dense-left-and-sparse-right (DLSR) framework and sparse-left-and-dense-right (SLDR) framework. As it turns out, the DLSR framework is more efficient than SLDR framework since one more step can be fully controlled, though with extra $$2^{32}$$ memory complexity. To construct the best differential characteristics for the DLSR framework, we carefully build the linearized part of the characteristics and then solve the corresponding nonlinear part using a guess-and-determine approach. Based on the newly discovered differential characteristics, we provide colliding messages pairs for the first practical collision attacks on 30 and 31 (out of 80) steps of RIPEMD-160 with time complexity $$2^{35.9}$$ and $$2^{41.5}$$ respectively. In addition, benefiting from the partial calculation, we can attack 33 and 34 (out of 80) steps of RIPEMD-160 with time complexity $$2^{67.1}$$ and $$2^{74.3}$$ respectively. When applying the SLDR framework to the differential characteristic used in the Asiacrypt 2017 paper, we significantly improve the time complexity by a factor of $$2^{13}$$ . However, it still cannot compete with the results obtained from the DLSR framework. To the best of our knowledge, these are the best collision attacks on reduced RIPEMD-160 with respect to the number of steps, including the first colliding message pairs for 30 and 31 steps of RIPEMD-160.
Visual Methods for the Design of Shape-Changing Interfaces

Shape-changing interfaces use physical change in shape as input and/or output. As the field matures, it will move from technology-driven design toward more formal processes. However, this is challenging: end-users are not aware of the capabilities of shape-change, devices are difficult to demonstrate, and presenting single systems can 'trap' user-thinking into particular forms. It is crucial to ensure this technology is developed with requirements in mind to ensure successful end-user experiences. To address this challenge, we developed and tested (n = 50) an approach that combines low-fidelity white-box prototypes and high-fidelity video footage with end-user diagram and scenario sketching to design context dependent devices. We analysed the outputs of our test process and identified themes in device design requirements, and from this constructed a shape-change stack model to support practitioners in developing, classifying, and synthesising end-user requirements for this novel technology.
TextMap: A general purpose visualization system

Human language is a versatile tool for communicating mental models between speakers of a language. This paper presents the TextMap system, a logic-based system for generating visuospatial representations from textual input. TextMap combines a minimalistic parser with a simple model counting mechanism to automatically extract coordinate information from propositional Horn-logic knowledge bases encoding spatial predications. The system is based on a biologically inspired low-level bit vector mechanism, the activation bit vector machine (ABVM). It does not require an ontology apart from a list of which tokens indicate relations. Its minimalism and simplicity make it a general purpose visualization or imagery tool. The paper describes the TextMap application architecture as well as the key algorithms forming the ABVM core. The system is evaluated with respect to a larger case study of a geographic layout of 13 cities demonstrating capabilities as well as current shortcomings for a complex scenario of a human mental map description.
A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction

Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persistent coverage is a variant of traditional coverage where coverage levels in the environment decay over time. Thus, robots have to continuously revisit parts of the environment to maintain a desired coverage level. Facilitating this in the real world demands we tackle numerous subproblems. While there exist standard solutions to these subproblems, there is no complete framework that addresses all of their individual challenges as a whole in a practical setting. We adapt and combine these solutions to present a planning framework for persistent coverage with multiple unmanned aerial vehicles (UAVs). Specifically, we run a continuous loop of goal assignment and globally deconflicting, kinodynamic path planning for multiple UAVs. We evaluate our framework in simulation as well as the real world. In particular, we demonstrate that (i) our framework exhibits graceful coverage—given sufficient resources, we maintain persistent coverage; if resources are insufficient (e.g., having too few UAVs for a given size of the environment), coverage levels decay slowly and (ii) planning with global deconfliction in our framework incurs a negligibly higher price compared to other weaker, more local collision-checking schemes (Video: https://youtu.be/aqDs6Wymp5Q ).
Towards Automatically Penalizing Multimedia Breaches (Extended Abstract)

This work studies the problem of automatically penalizing intentional or unintentional data breach (APB) by a receiver/custodian receiving confidential data from a sender. We solve this problem for multimedia data by augmenting a blockchain on-chain smart contract between the sender and receiver with an off-chain cryptographic protocol, such that any significant data breach from the receiver is penalized through a monetary loss. Towards achieving the goal, we use an oblivious transfer scheme when used with a robust watermarking and a claim-or-refund blockchain contract provides the necessary framework to realize the APB protocol in a provably secure manner. In our APB protocol, a public data breach by the receiver leads to her Bitcoin (or other blockchain) private signing key getting revealed to the sender, which allows him to penalize the receiver by claiming the deposit from the claim-or- refund contract. Interestingly, the protocol also ensures that the malicious sender cannot steal the deposit, even as he knows the original multimedia document or releases it in any form.
Three-Dimensional Mineral Potential Mapping for Reducing Multiplicity and Uncertainty: Kaerqueka Polymetallic Deposit, QingHai Province, China

This study focuses on the selection of a method combining simulations of mineralizing processes with three-dimensional (3D) mapping to quantitatively predict locations of concealed mineralization and to reduce uncertainty and multiplicity. The simulation of ore-forming processes for concealed mineralization requires the assessment of a region's structural geology, ages of lithological units, degree of metamorphism, and role of magmatism in the genesis of mineralization. The evolution of various factors affecting the development of geological processes in space and time is discussed, including the assessment of models combining regional deformation-magmatic activity-mineralized hydrothermal fluid based on empirical knowledge. Given that metallogeny is a study of the genesis and regional distribution of mineral deposits and emphasizes their spatial and temporal relationships, this paper uses empirical knowledge to predict the locations of concealed mineral deposits. Furthermore, the analysis of 3D geological modeling of concealed orebodies is a process that contributes to understanding the metallogenesis of mineral deposits. In this study of the Kaerqueka skarn and porphyritic deposits, geological, geographic, remote sensing and geochemical data collected from this region are processed using computer modeling techniques available in the Surpac software to establish a digital representation of the mineral field. The "block mapping modeling" theory, geostatistical methods, simulations of ore-forming processes, and 3D mapping methods are then combined to predict the locations of the most promising areas for concealed mineral deposits around the Kaerqueka deposit. This research introduces a new method for predicting and evaluating the locations of concealed orebodies by reducing multiplicity and uncertainty.
“What”, “Where”, and “Why” Cybersecurity Controls to Enforce for Optimal Risk Mitigation

Most enterprises depend on information security standards and guidelines such as CIS Critical Security Controls (CIS CSC)to adopt and manage cybersecurity controls. With the escalation and diversity of cyber attacks, hundreds of cybersecurity controls have been defined to implement NIST Cybersecurity Framework (i.e., Identify, Detect, Protect, Respond, and Recover)[1]. However, the selection of the most appropriate set of security controls to optimize cyber defense Return on Investment (ROI)is still a highly complex and error-prone task due to the large number of security controls, the consideration of various risk factors (such as vulnerabilities and attack incidents), and budget constraints. Moreover, the complexity exacerbates due to the presence of various enterprise-oriented usability requirements. In this paper, we present a novel model and optimization techniques to select the most cost-effective set of Critical Security Controls (CSC) for optimal risk mitigation planning considering affordable residual risk, budget, and usability constraints. We developed Cyber Defense Matrix (CDM), that our automated approach uses to determine “what” security controls are needed at a particular enterprise for security function (Identify, Protect, Detect, Respond, and Recover), “where” to enforce in the cyber environment (Network, Device, People, Application, and Data), and “why” it is effective in the cyber attack kill chain phases. We formulate the CDM decision-making problem using SMT constraints and developed a tool, called CyberARM, that computes correct-by-construction planning to satisfy cybersecurity ROI with bounded residual risk under specific budget constraints.
Data mining in IoT era:A method based on improved frequent items mining algorithm

This paper focuses on big data in IoT. With the development and popularization of 5G technology, IoT based on 5G technology will be popularized. At the same time, it will increase more use of big data that come from heterogeneous devices. Although it is now a big data era, data from IoT will also explode in future. In these IoT data, the first task that we should do is to mine the related items and to find the association rules. So we proposed an improved frequent items mining algorithm that based on traditional Apriori algorithm. Considering time series analysis, the IoT data is coarse-grained, so it’s hard to find frequent items in the data. Based on this, we divide data into different subsets based on different times and set an initial threshold. We propose an improved frequent items and association items mining algorithm-ADAA(advanced Apriori algorithm).
Virtual Reality and Anxiety Disorders Treatment: Evolution and Future Perspectives

Virtual reality (VR) is a technology that allows the simulation of different real-life situations on a tridimensional computer-generated environment where the user can interact with the environment as if he/she were the real world. VR has potential as an exposure technique for treating anxiety disorders because VR and real objects have similar characteristics, which creates the illusion that the user is immersed and engaged with objects in the real world. Regarding the efficacy of using virtual reality exposure-based therapy (VR-EBT), for more than two decades, there has been sufficient empirical evidence regarding VR-EBT for treating anxiety disorders. Finally, this chapter ends with some directions and perspectives for future VR-EBT developments and treatments protocols.
The Chinese Knowledge Graph on Domain-Tourism

Tourism plays an increasingly important role in people’s daily life. However, in the era of big data, tourists find, it is difficult to acquire useful knowledge of travel information on the Internet. Knowledge Graph describes the real-world concepts, entities and their relationships in a structured form. Which is capable of mapping the Internet information in the form that is closer to the human cognition. Thus, provides the ability to organize, manage and understand the vast travel information available on the Internet. In order to promote the sharing of tourism knowledge and culture in China, we are committed to constructing the Chinese knowledge graph on domain-tourism, in which knowledge is obtained from the existing Chinese encyclopedia knowledge graph and unstructured web pages. In addition, we provide a storage scheme of RDF triples data in the graph database and build the tourism knowledge application platform based on it.
Can Analytics as a Service Save the Online Discussion Culture? - The Case of Comment Moderation in the Media Industry

In recent years, online public discussions face a proliferation of racist, politically, and religiously motivated hate comments, threats, and insults. With the failure of purely manual moderation, platform operators started searching for semi-automated or even completely automated approaches for comment moderation. One promising option to (semi-) automate the moderation process is the application of Natural Language Processing and Machine Learning (ML) techniques. In this paper we describe the challenges, that currently prevent the application of these techniques and therefore the development of (semi-) and automated solutions. As most of the challenges (e.g., curation of big datasets) require huge financial investments, only big players, such as Google or Facebook, will be able to invest in them. Many of the smaller and medium-sized internet companies will fall behind. To allow this bulk of (media) companies to stay competitive, we design a novel Analytics as a Service (AaaS) offering that will also allow small and medium sized enterprises to profit from ML decision support. We then use the identified challenges to evaluate the conceptual design of the business model and highlight areas of future research to enable the instantiation of the AaaS platform.
Parallel Adaptive Sampling with Almost No Synchronization

Approximation via sampling is a widespread technique whenever exact solutions are too expensive. In this paper, we present techniques for an efficient parallelization of adaptive (a.k.a. progressive) sampling algorithms on multi-threaded shared-memory machines. Our basic algorithmic technique requires no synchronization except for atomic load-acquire and store-release operations. It does, however, require $$\mathcal {O}(n)$$ memory per thread, where n is the size of the sampling state. We present variants of the algorithm that either reduce this memory consumption to $$\mathcal {O}(1)$$ or ensure that deterministic results are obtained. Using the algorithm for betweenness centrality (a popular measure in network analysis) approximation as a case study, we demonstrate the empirical performance of our techniques. In particular, on a 32-core machine, our best algorithm is $$2.9{\times }$$ faster than what we could achieve using a straightforward OpenMP-based parallelization and $$65.3{\times }$$ faster than the existing implementation of .
Convolutional Sparse Coding Using Wavelets for Single Image Super-Resolution

In this paper, we propose the convolutional sparse coding based model in the wavelet domain for the task of single image super-resolution (SISR). The conventional sparse coding based approaches work on overlapping image patches and use the dictionary atoms to sparse code an image patch. Further, at the final stage, an overlap-add mechanism is used to get the final high-resolution image estimate. However, these algorithms fail to take into account the consistency present in the overlapping patches which limits their performance. We propose the use of wavelet integrated convolutional sparse coding approach where instead of dictionary atoms we utilize the convolution summations between the learned filters and their mappings for sparse representation based SISR. The use of wavelets is proposed owing to their unique directional and compact features. A pair of filters are learned along with a mapping function for each wavelet sub-band to exploit the consistency among patches. The proposed wavelet integrated convolutional sparse coding model helps capture useful contextual information. The proposed model is evaluated on publicly available datasets for different scale-up parameters. To show the efficacy of the proposed model we compare it with recent state-of-the-art algorithms. The visual results along with the quantitative ones indicate that the proposed model performs well for the tasks of super-resolution.