Reviewerly/exHarmony huggingface dataset:
ELM-Kernel and reduced kernel ELM based watermarking scheme

With high popularity of Internet, digital data or information can be shared at much higher speed, with less expenses and efforts. This creates new opportunities like e-advertisement, web publishing, audio and video deliveries but these opportunities create many new threats also. An important issue is the copyright protection of the content. Digital watermarking is one of the approach gaining interest to achieve the security of the content. For copyright protection, the blind digital watermarking schemes with the new machine learning technique ELM-Kernel(KELM) and Reduced Kernel Extreme Learning Machine(RKELM) plus Integer Wavelet Transformation(IWT) and Singular Value Decomposition(SVD) have been proposed. The scheme is composed of three steps, namely, watermark embedding, machine learning phase and finally an extraction of watermark. In the embedding phase, the cover image is transformed into IWT domain, then the HL sub-band has been divided into 4 × 4 non-overlapping blocks. SVD is applied to each block to get their singular values then these values have been modified by using the Cox's formula to embed one bit of watermark in one block and a watermarked image is then obtained. During the machine training phase, an ELM-Kernel(KELM) and Reduced Kernel Extreme Learning Machine(RKELM) is trained separately to memorize the relationship between the coefficients of watermarked image and the original cover image. During extraction phase, an oblivious phase, where the original image is not required and the trained machine is used to extract the watermark from the watermarked image. This algorithm aims to minimize the value of BER in order to assess the robustness of the algorithm. Results show that value of BER is very small even watermarked images are subject to attacks like noise, cropping, sharpening, rotation and blurring. The extracted watermarks are shown in the results which has great similarity with the original watermark. Performance of the proposed method is analyzed by the comparative study with some of the existing methods and it demonstrates that the proposed method outperforms these methods in terms of robustness.
Improving Efficiency of Parallel Vertex-Centric Algorithms for Irregular Graphs

Memory access is known to be the main bottleneck for shared-memory parallel graph applications especially for large and irregular graphs. Propagation blocking (PB) idea was proposed recently to improve the parallel performance of PageRank and sparse matrix and vector multiplication operations. The idea is based on separating parallel computation into two phases, binning and accumulation, such that random memory accesses are replaced with contiguous accesses. In this paper, we propose an algorithm that allows execution of these two phases concurrently. We propose several improvements to increase parallel throughput, reduce memory overhead, and improve work efficiency. Our experimental results show that our proposed algorithms improve shared-memory parallel throughput by a factor of up to 2× compared to the original PB algorithms. We also show that the memory overhead can be reduced significantly (from 170 percent down to less than 5 percent) without significant degradation of performance. Finally, we demonstrate that our concurrent execution model allows asynchronous parallel execution, leading to significant work efficiency in addition to throughput improvements.
Addressing Fine-Grained Variability in User-Centered Software Product Lines: A Case Study on Dashboards

Software product lines provide a theoretical framework to generate and customize products by studying the target domain and by capturing the commonalities among the potential products of the family. This domain knowledge is subsequently used to implement a series of configurable core assets that will be systematically reused to obtain products with different features to match particular user requirements. Some kind of interactive systems, like dashboards, require special attention as their features are very fine-grained. Having the capacity of configuring a dashboard product to match particular user requirements can improve the utility of these products by providing the support to users to reach useful insights, in addition to a decrease in the development time and an increase in maintainability. Several techniques for implementing features and variability points in the context of SPLs are available, and it is important to choose the right one to exploit the SPL paradigm benefits to the maximum. This work addresses the materialization of fine-grained variability in SPL through code templates and macros, framed in the particular domain of dashboards.
A Refined Interpretation of Intuitionistic Logic by Means of Atomic Polymorphism

We study an alternative embedding of IPC into atomic system F whose translation of proofs is based, not on instantiation overflow, but instead on the admissibility of the elimination rules for disjunction and absurdity (where these connectives are defined according to the Russell–Prawitz translation). As compared to the embedding based on instantiation overflow, the alternative embedding works equally well at the levels of provability and preservation of proof identity, but it produces shorter derivations and shorter simulations of reduction sequences. Lambda-terms are employed in the technical development so that the algorithmic content is made explicit, both for the alternative and the original embeddings. The investigation of preservation of proof-reduction steps by the alternative embedding enables the analysis of generation of “administrative” redexes. These are the key, on the one hand, to understand the difference between the two embeddings; on the other hand, to understand whether the final word on the embedding of IPC into atomic system F has been said.
Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research

The paper presents an approach to knowledge management in learning and scientific research that allows increasing the availability of expert knowledge and reducing semantic noise during knowledge transfer. The availability of expert knowledge is ensured by transforming them from implicit into explicit form (in the form of Semantic Link Network). The reduction of semantic noise is achieved through the integration of knowledge in different forms and their personalization for different groups of knowledge recipients. In the paper, the tasks of decision-making support are formulated which should be performed by the knowledge management system on the base of the proposed approach.
Ina-BWR: Indonesian bigram word rule for multi-label student complaints

Handling multi-label student complaints is one of interesting research topics. One of techniques used for handling multi-label student complaints is Bag of Word (BoW) method. In this research bigram word rule and preprocess are proposed to increase the accuracy of multi-label classification results. To show the effectiveness of the proposed method, data from Telkom University student data and additional relevant data by using hashtag are used as testing data. We develop Indonesian Bigram Word Rule for Multi-label Student Complaints (Ina-BWR) to identify multi-label student problems based on Bigram Word Rule. Ina-BWR consists of three processes such as preprocessing informal text, identifying complaint and object from text. Additional preprocessing techniques are conducted to formalize the text such as parsing a hashtag, correcting affixes word, correcting a conjunction word, parsing suffix people pronoun and correcting typo words. Indonesian bigram word rule is adopted from opinion identification rules with 3 additional corpuses (-)NN, (-)JJ and (-)VB to identify student complaints. To identify complaints, four label corpuses have been created manually. The experimental results show that Ina-BWR can increase Personal, Subject and Relation label accuracies. The best accuracy for four labels is obtained when Ina-BWR is combined with BoW method.
Hand Pose Detection in HMD Environments by Sensor Fusion using Multi-layer Perceptron

The paper proposes a sensor fusion method to detect the pose of user's hand in head-mount display environments by using a Leap Motion Camera (LMC) with simple circular artificial markers on surfaces of a box wearing on the back of hand and two IMU sensors. One IMU sensor is located on the box and the other IMU sensor is fixed with the camera. Multilayer Perceptron (MLP) is adopted to transform the hand's pose in IMU coordinate system into the pose in global coordinate system attached at LMC by minimizing Mean Square Error (MSE) for Virtual Reality (VR)/Mixed Reality (MR) applications. The pose detection results are compared with poses of bare hand captured by LMC while the estimated data after transformation is fitted well with reference data in the sense that the average of mean difference for each roll, pitch and yaw angle is around 3.54 degree. It is applied successfully to track and estimate the pose of user's hand in around 70Hz.
Stream Parallelism on the LZSS Data Compression Application for Multi-Cores with GPUs

GPUs have been used to accelerate different data parallel applications. The challenge consists in using GPUs to accelerate stream processing applications. Our goal is to investigate and evaluate whether stream parallel applications may benefit from parallel execution on both CPU and GPU cores. In this paper, we introduce new parallel algorithms for the Lempel-Ziv-Storer-Szymanski (LZSS) data compression application. We implemented the algorithms targeting both CPUs and GPUs. GPUs have been used with CUDA and OpenCL to exploit inner algorithm data parallelism. Outer stream parallelism has been exploited using CPU cores through SPar. The parallel implementation of LZSS achieved 135 fold speedup using a multi-core CPU and two GPUs. We also observed speedups in applications where we were not expecting to get it using the same combine data-stream parallel exploitation techniques.
Slide attacks and LC-weak keys in T-310

T-310 is an important Cold War cipher (Cryptologia 2006). In a recent article (Cryptologia 2018), researchers show that, in spite of specifying numerous very technical requirements, the designers do not protect the cipher against linear cryptanalysis and some 3% of the keys are very weak. However, such a weakness does not necessarily allow breaking the cipher because it is extremely complex and extremely few bits from the internal state are used for the actual encryption. In this article, we finally show a method that allows recovering a part of the secret key for about half of such weak keys in a quasi-realistic setting. For this purpose, we revisit another recent article from Cryptologia from 2018 and introduce a new peculiar variant of the decryption oracle slide attack with d = 0.
Efficient and Precise Information Flow Control for Machine Code through Demand-Driven Secure Multi-Execution

Dynamic Information Flow Control (IFC) systems, like No-Sensitive-Upgrade or Permissive-Upgrade, can guarantee Termination-Insensitive Non-Interference, but reject valid programs due to their inability to track implicit flows. More advanced multi-execution based approaches, like Shadow Execution and Secure Multi-Execution, are precise and guarantee Termination-Sensitive Non-Interference, but require additional resources or, in the case of Faceted Evaluation, deep changes to the execution semantics. In this paper, we propose a novel efficient and precise Information Flow Control system for machine code through Demand-Driven Secure Multi-Execution. Our key idea is to use lightweight single-execution monitoring as long as the execution is secretless and fork multiple copies on-demand when necessary. We present the first Secure Multi-Execution implementation for legacy code in Unix-based environments and show that our demand-driven optimization drastically reduces the run-time overhead for cat and sha256sum. Our results indicate that further acceleration is possible through improved static analyses, making multi-execution based IFC systems applicable to machine code.
All work and no play: A text analysis

Automated analysis of open-ended text survey data is an appealing prospect. It eliminates human error and human variability and can be used to create models that are easier to update over time than a manual approach to coding generally yields. Today, text analytics is a huge business and is among the most popular innovations within the current research landscape. However, within the research industry, there has been little change in usage in recent years, and awareness of the options available appears to be limited. We wished to look more closely at the true strengths of different approaches, the main barriers to their adoption, and how these might be overcome. Using text responses from a short survey about work and play in two markets, we contrasted two tools in analyzing the output: Q’s text analysis component and Google Cloud Natural Language. We chose these tools as they can each be easily applied to survey data but are based on different analytic principles. We found some surprising differences between the output of the two tools and between the text analysis metrics and scalar data. We conclude by discussing some of the key contemporary themes in text analytics and the likely future role of this method within market research and insight.
A new region based watermarking scheme for medical images

The widespread use of information and communication technologies in Healthcare services such as telemedicine has made it easy to intercept and tamper sensitive medical data when public networks are used. Thus, there is an urgent need of security measures to address the requirements of both safety and privacy of medical information. In such a situation, watermarking has been proposed as one of the most promising techniques to provide security, reliability, and authenticity of medical information. In this paper, we propose a new region based watermarking technique for medical images. The proposed algorithm is based on the polynomial decomposition. We initially introduce the mathematical model of the polynomial decomposition and its properties, which consists of decomposing the medical image on two components: geometrical part and smooth part. Then we provide the necessary algorithms for watermark insertion and extraction. Compared with the state-of-the art methods, experimental results reveal that the proposed algorithm can achieve good results in terms of invisibility, authenticity, confidentiality and robustness of the watermark.
Environment mediated multipartite and multidimensional entanglement

Abstract Quantum entanglement is usually considered a fragile quantity and decoherence through coupling to an external environment, such as a thermal reservoir, can quickly destroy the entanglement resource. This doesn't have to be the case and the environment can be engineered to assist in the formation of entanglement. We investigate a system of qubits and higher dimensional spins interacting only through their mutual coupling to a reservoir. We explore the entanglement of multipartite and multidimensional system as mediated by the bath and show that at low temperatures and intermediate coupling strengths multipartite entanglement may form between qubits and between higher spins, i.e., qudits. We characterise the multipartite entanglement using an entanglement witness based upon the structure factor and demonstrate its validity versus the directly calculated entanglement of formation, suggesting possible experiments for its measure.
Application of GIS for the mapping of landslide-vulnerable areas by through android-based Analytical Hierarchy Process (AHP) method in Bantul Regency

Landslide is a general term that covers a wide variety of soil forms and processes involving earth, rock, or debris movements falling on the lower slopes below due to the influence of gravity. This disaster occurs in many parts of Indonesia, including in Bantul Regency due to its relatively unstable land and vulnerability to land movement. In addition, a landslide disaster can lead to casualties and considerable material losses. This study aims to build an Android-based GIS application that can map landslide-vulnerable areas by dividing the vulnerability into three categories, namely low, medium, and high, using the Analytical Hierarchy Process (AHP) method. This research is expected to be useful for the community and the Government in order to be alert and responsive to handle landslide disasters in areas that have been declared vulnerable to landslide disasters. The results of research by using the GIS application produced showed that from 17 districts in Bantul Regency, 6 are districts that have a high vulnerability level of landslide disasters. The six districts are Pleret, Imogiri, Kretek, Sanden, Srandakan, and Bambanglipuro.
Automated feedback generation for formal manufacturing rule extraction

Abstract Manufacturing knowledge is maintained primarily in the unstructured text in industry. To facilitate the reuse of the knowledge, previous efforts have utilized Natural Language Processing (NLP) to classify manufacturing documents or to extract structured knowledge (e.g. ontology) from manufacturing text. On the other hand, extracting more complex knowledge, such as manufacturing rule, has not been feasible in a practical scenario, as standard NLP techniques cannot address the input text that needs validation. Specifically, if the input text contains the information irrelevant to the rule-definition or semantically invalid expression, standard NLP techniques cannot selectively derive precise information for the extraction of the desired formal manufacturing rule. To address the gap, we developed the feedback generation method based on Constraint-based Modeling (CBM) coupled with NLP and domain ontology, designed to support formal manufacturing rule extraction. Specifically, the developed method identifies the necessity of input text validation based on the predefined constraints and provides the relevant feedback to help the user modify the input text, so that the desired rule can be extracted. We proved the feasibility of the method by extending the previously implemented formal rule extraction framework. The effectiveness of the method is demonstrated by enabling the extraction of correct manufacturing rules from all the cases that need input text validation, about 30% of the dataset, after modifying the input text based on the feedback. We expect the feedback generation method will contribute to the adoption of semantics-based technology in the manufacturing field, by facilitating precise knowledge acquisition from manufacturing-related documents in a practical scenario.
Effective Feature-Based Automatic Modulation Classification Method Using DNN Algorithm

In this paper, we propose an effective feature-based automatic modulation classification (AMC) method using a deep neural network (DNN). In order to classify the modulation type, we consider effective features according to the modulation signals. The proposed method removes the meaningless features that have little influence on the classification and only uses the effective features that have high influence by analyzing the correlation coefficients. From the simulation results, we observe that the proposed method can make the AMC system low complexity.
Deep learning for object detection in video

With the development of deep learning technology in recent years, more and more fields are introducing deep learning to accelerate the development of the industry. Deep learning technology was originally used for image recognition. Nowadays, with the continuous improvement of video monitoring, the application of deep learning to surveillance video has profound implications for areas such as transportation and unmanned vehicle. The effects of traditional video recognition are not satisfactory, and deep learning is excellent in many image classification scenes. In this paper, we proposed an object detection algorithm of video based on YOLO network to realize vehicle identification based on surveillance video.
Bengali Text Summarization using TextRank, Fuzzy C-Means and Aggregate Scoring methods

In this world, it is very difficult and time consuming for humans to summarize large documents, reports, news and research articles. Multiple text summarization techniques play vital roles in picking the important points and sentences thus reducing the time and effort required to read a whole article. Numerous summarization techniques have been applied to the English language but works on Bangla text summarization is still limited. Furthermore, in our country, Bangladesh, all summarization is mainly done by humans. Keeping that in mind we aim to find a simple way of summarizing Bengali texts with the technology at hand. Text summarization can be of two types, abstractive and extractive. In this paper, we will use extractive text summarization to summarize Bengali passages, using Fuzzy C-Means, TextRank and Aggregate Sentence Scoring methodologies. We have also done a comparative study, among the three methodologies to find out that Fuzzy C-Means out performs the other two methods to generate a more concise and accurate summary.
Anticipating Precarity and Risk in Social Innovation Design for Entrenched Place-Based Disadvantage

We outline considerations for anticipating precarity and risk in design for social innovation through a case study tackling place-based disadvantage for young people with The Australian Centre for Social Innovation (TACSI). In shifting the focus from creative or managed risk within the design process, we ask: what practices of decision-making can lay alternative foundations for change for those who are experiencing precarious lives? We contribute an alternative conceptual framing of risk by drawing from Judith Butler’s position on precarity as a heightened attentiveness to complex interconnected concerns. In moving towards transformational design, we argue for practices that work through precarity to offer more nuanced understandings of risk when seeking to develop sustainable change. From the perspective of a design team in the midst of a project, we reflect on the value of responding to risk in relation to precarity, highlighting intersectional concerns that support ongoing decision-making. With direct measures of what is considered successful and valuable from design interventions, what becomes most precarious is the lack of recognition of complex personhood, translation through design, and opportunities for working across multiple sites of experimental intervention.
Auction‐based serious game for bug tracking

Today, one of the challenges in software engineering is utilising application lifecycle management (ALM) tools effectively in software development. In particular, it is hard for software developers to engage with the work items that are appointed to themselves in these ALM tools. In this study, the authors have focused on bug tracking in ALM where one of the most important metrics is mean time to resolution that is the average time to fix a reported bug. To improve this metric, they developed a serious game application based on an auction-based reward mechanism. The ultimate aim of this approach is to create an incentive structure for software practitioners to find and resolved bugs that are auctioned where participants are encouraged to solve and test more bugs in less time and improve quality of software development in a competitive environment. They conduct hypothesis tests by performing a Monte Carlo simulation. The preliminary results of this research support the idea that using a gamification approach for an issue tracking system enhances the productivity and decreases mean time to resolution.