Reviewerly/exHarmony huggingface dataset:
Improvement of Collaborative Filtering Recommendation System to Resolve Sparsity Problem using Combination of Clustering and Opinion Mining Methods

Online shopping or e-commerce sites are developing quite rapidly along with the development of the internet and technology in Indonesia. However, buyers in e-commerce still have problems trusting when buying products. Feedback from other buyers, either comments or ratings, is considered capable of influencing the behavior of buyers in buying items. In addition, a system of recommendations is known to help the buyer's decision to choose the product to be purchased. Collaborative Filtering (CF) is one of the popular recommendation methods. However, the ability of CF is limited by the problem of sparsity. In this study, we proposed model-based CF recommendation system that has good accuracy and quality recommendations by combining clustering methods using k-means++ to reduce data dimensions and opinion mining or sentiment analysis using Multinomial Naïve Bayes to filter recommendation results. K-means++ was chosen because it proved to have good quality clustering results, while Multinomial Naïve Bayes was chosen because of its simplicity and good accuracy in processing data used. This study used user-item interaction data and product reviews from Bukalapak. The experimental results showed that the proposed CF recommendation system had good accuracy with f-measure about 0.45 and quality improvement about 28%.
I Want To Ride My Bicycle – I Want To Ride My Bike Using low cost interfaces for Virtual reality

The paper will give an overview of our experiments in the past years in developing different interfaces and workflows for the use of low cost Head Mounted Displays (HMD) for Virtual Reality solution.We are mainly interested in using VR tools for designers in the early phases of their design.In our opinion VR tools can help to bring back a better understanding of space and scale which have been lost a little bit in the last century with the change from analogue to digital tools.After teaching architectural and urban design for many years we can clearly say that this effect is still ongoing and it is time that we develop digital tools that try to reverses thi effect.We will then concentrate within this paper on discussing some aspects of data reduction that are important to be able to use these tools in the design process.We are also showing how we use our interfaces presenting some results of student projects for a design in Hong Kong and the strategies and methods for using VR for a ongoing work on a project about the establishment of a so called ``bicycle highway'' in the city of Graz in Austria.
A Denoising Framework for Image Caption

Image caption is one of the hottest research topics at the moment in the image processing field. However, most image caption models based on the Encoder-Decoder framework cannot accurately find the alignment relationship between objects in the image and objects in the text, resulting in an inaccurate description. In this work, we propose a denoising framework in which the image and the text are processed separately, and we use the caption stem to find the alignment relationship between image and text accurately. Compared with previous work, this framework can more accurately find the alignment of images and texts and generate more accurate captions. Experiments on the MSCOCO dataset show that the caption generated by this model can express the content of an image more accurately. Besides, the model can generate short captions and long captions with rich semantic according to users' needs.
(Linkable) Ring Signature from Hash-Then-One-Way Signature

In this paper, we revisit the generic construction of ring signatures from hash-then-one-way type (Type-H) signatures proposed by Abe et al. (AOS) in 2004 and made the following contributions. First, we give a proof for the generic construction, in a strengthened security model. Previously, this was only done for concrete instantiations, in a weaker model. Second, we extend AOS's framework to generically construct one-time linkable ring signatures from Type-H signatures and one-time signatures. Lastly, we instantiate the generic construction with an NTRU-based Type-H signature: Falcon~and obtain a post-quantum linkable ring signature scheme. Our analysis shows that the resulting linkable signature is more efficient than any existing lattice based solutions for small to moderate number of users.
Latent Class Models on Business Analytics

This paper discusses the systematic application of the latent class model on business analytics. The latent class model is one of the effective statistical model classes on business analytics to represent essential statistical structures by learning the sparse and high dimensional data. This model class is useful for the purpose of reduction of feature dimension and cancellation of sparseness of the data. This is because many practical data can be assumed to consist of several unobserved subgroups. For example, a customers set, which is a target in the field of marketing analysis, consists of several subgroups with different characteristics and preferences. In addition, data clustering can also be realized by estimated belonging probabilities to latent classes of each data.This paper gives a general form of the latent class model and discuss how to construct the model structure and apply to a real problem in business analytics. After describing important points to be noted in analysis based on a latent class model, several practical examples are also shown.
Modal Schema Graphs for Graph Databases

Although graph databases are conceived schema-less, additional knowledge about the data’s structure and/or semantics is beneficial in many graph database management tasks, from efficient storage, over query optimization, up to data integration. Today’s commonly used graph data models do not represent primal suspects regarding their lack of schema prior to data population. More than 20 years ago, also semistructured data has been introduced without an a-priori conceptual modeling phase. Neat models, called schema graphs, have been proposed and proven useful, yet heavily relying on the employed data model, which had been rooted labeled graphs. We generalize schema graphs in two respects: (1) Our notions are based on labeled graphs because the root node assumption is invalid in the spirit of modern graph data models. (2) We propose and study modal schema graphs to increase the expressive power of the original model. Modal schema graphs allow for (conditional) structural requirements without an otherwise necessary logical device. Furthermore, we elaborate on the consequences of our expressiveness enhancement with respect to applications and algorithmic complexity.
Energy-Aware Core Switching for Mobile Devices with a Heterogeneous Multicore Processor

Mobile devices should be designed to meet two contradictory goals: providing the performance desired by users while minimizing the energy consumption to meet the users' expectation of the battery run-time. The heterogeneous multicore architecture has been introduced to mobile world for higher energy-efficiency and performance. It is possible, however, only when the operating system assigns the appropriate core for an application to take full advantage of the heterogeneity. This article addresses the problem of the core selection for mobile devices with a heterogeneous multicore processor. It is shown that core switching for less energy consumption must consider both core frequency and utilization at the same time.
Minimal Controllable Set for Takagi-Sugeno Fuzzy Systems with disturbances

In this paper, an approximation of the smallest set (in a given norm) to which the state of a system can be steered (and kept inside once reached) is provided, when bounded disturbances are present. A non-LMI geometric polytope-manipulation approach is pursued over a Takagi-Sugeno model in order to solve this disturbance-rejection setup. Once this set is available, the techniques in this paper compute, too, explicit controllers driving initial conditions to such set, as well as controllers that make this set invariant. This work generalises the earlier particular undisturbed case (i.e., just stabilization) developed in Ariño et al. (2017b).
Towards Maximal Service Profit in Geo-Distributed Clouds

With the proliferation of globally-distributed services and the quick growth of user requests for inter-datacenter bandwidth, cloud providers have to lease a good deal of bandwidth from Internet service providers to satisfy the user demands. Neither maximizing the service revenue nor minimizing the service cost can bring the maximal service profit to cloud providers. The diversity of user requests and the large unit of inter-datacenter bandwidth further increase the difficulty of scheduling user requests. In this paper, we propose a cloud operational model to help cloud providers to make more service profit by properly selecting requests to serve rather than serving all user requests. We formulate the problem of service profit maximization and prove its NP-hardness. Considering the complicated coupling between maximizing revenue and minimizing cost, we propose a framework, Metis, for the efficient scheduling of user requests over inter-datacenter networks to maximize the service profit for cloud providers. Metis is formed with the alternate operations of two algorithms derived from randomized rounding techniques and Chernoff-Hoeffding bound. We prove that they can provide the guarantees on approximation ratios. Our extensive evaluations demonstrate that Metis can achieve more than 1.3x the service profits of existing solutions.
Evaluation Model of Maritime Search and Rescue Response Capability

Considering the problems of randomness and fuzziness in evaluating indexes from maritime search and rescue response capability system, a novel method based on subjective-objective attribute weights integrated and center normal grey cloud method was presented. Firstly, cloud theory was introduced to transform the uncertainty of factors into quantitative digital features, to achieve the qualitative indicators of quantitative processing. Secondly, the improved analytic hierarchy process(IAHP) and criteria improved through inter-criteria correlation(CRITIC) were used respectively to determine the subjective and objective weights. The comprehensive weights were thus obtained through the relative information entropy principle. Thirdly, grey cloud method was used to make the second level evaluation indexs to get the assessment of maritime search and rescue response capability. And on the basis of that, the maritime search and rescue response capability evaluation results was obtained. Finally, the model was evaluated on three ports and the results showed the IAHP-CRITIC combination weighting method can effectively integrate the subjective and objective weights information. Compared with the other traditional models, our proposed model in this paper is closer to the actual situation which can comprehensively consider grey, fuzziness and randomness in the evaluation process. The above illustrative example analysis shows the effectiveness and feasibility of this method.
Testability First!

Background: The pivotal role of testing in high-quality software production has driven a significant effort in evaluating and assessing testing practices. Aims: We explore the state of testing in a large industrial project over an extended period. Method: We study the interplay between bugs in the project and its test cases, and interview developers and stakeholders to uncover reasons underpinning our observations. Results: We realized that testing is not well adopted, and that testability(i.e., ease of testing) is low. We found that developers tended to abandon writing tests when they assessed the effort to be high. Frequent changes in requirements and pressure to add new features also hindered developers from writing tests. Conclusions: Regardless of the debates on test first or later, we hypothesize that the underlying reasons for poor test quality are rooted in a lack of attention to testing early in the development of a software component, leading to poor testability of the component. However, testability is usually overlooked in research that studies the impact of testing practices, and should be explicitly taken into account.
Phase Extraction Algorithm Based on the Spatial Carrier-Frequency Phase-Shifting Gradient

For improving the accuracy and processing speed of phase extraction, a new phase extraction algorithm based on the spatial carrier-frequency phase-shifting gradient (SCFPS-G) is proposed. First, three-frame sub-interferograms along the direction of spatial carrier-frequency phase-shifting gradient (SCFPS-G), which can be constructed by the differential method from only one-frame spatial carrier-frequency interferogram (SCFI). And then, the measured phase will be extracted by unknown phase-shifting algorithms. Compared with the existing spatial carrier-frequency phase-shifting (SCFPS) algorithms, the proposed SCFPS-G algorithm not only can improve the efficiency of constructing sub-interferograms, but also decrease one-frame sub-interferogram requirement for phase extraction, so the data redundancy of interferogram and the influence of noise will be reduced. Specially, it is found even if the carrier frequency becomes very small, corresponding to the large spatial bandwidth utilization of image sensor, the proposed SCFPS-G algorithm still can work well. And the obvious advantages of the SCFPS-G algorithm in accuracy, processing time and feasibility of phase extraction can be demonstrated by the numerical simulations and the experimental results.
General Video Game AI Controller - Integrating Three Algorithms to Bring a New Solution

This paper presents the framework, rules, games, controllers, and results of the General Video Game Playing Competition. The competition proposes the challenge of creating controllers of an artificial intelligence for general video game play, where a single agent must be able to play many different and wide games, some of them unknown games as well as in principle unlimited range of games. This work describes the implementation of the controller by using A-Star Pathfinding, Monte Carlo and Genetic Algorithm. We also use combinations of the above mentioned algorithm to achieve better results. The paper also analyzes the performance of the different approaches submitted, and finally proposes future tracks for the competition.
TFix: Automatic Timeout Bug Fixing in Production Server Systems

Timeout is widely used to handle unexpected failures in distributed systems. However, improper use of timeout schemes can cause serious availability and performance issues, which is often difficult to fix due to lack of diagnostic information. In this paper, we present TFix, an automatic timeout bug fixing system for correcting misused timeout bugs in production systems. TFix adopts a drill-down bug analysis protocol that can narrow down the root cause of a misused timeout bug and producing recommendations for correcting the root cause. TFix first employs a system call frequent episode mining scheme to check whether a timeout bug is caused by a misused timeout variable. TFix then employs application tracing to identify timeout affected functions. Next, TFix uses taint analysis to localize the misused timeout variable. Last, TFix produces recommendations for proper timeout variable values based on the tracing results during normal runs. We have implemented a prototype of TFix and conducted extensive experiments using 13 real world server timeout bugs. Our experimental results show that TFix can correctly localize the misused timeout variables and suggest proper timeout values for fixing those bugs.
Development of a Simulation Tool to Estimate Electricity Consumption and Determine the Optimum Cooling System for Data Centers

Electricity consumption of datacenters is increasing significantly with the growth in Information Technology sector. Electricity consumption of datacenters occupies approximately 2 % of total electricity consumption in the USA which makes IT Sector second most electricity-consuming sector. Cooling system in a traditional datacenter is responsible of 30-60% of final electricity consumption. In this study, a software is developed to estimate the final energy demand of a datacenter and electricity consumption of the datacenter for variable cooling systems by using Visual Studio C# interface and SQL server. Climate data, physical properties of the datacenter building and IT equipment situated in datacenter inputs are used in order to software to calculate annual energy demand of datacenter. Then electricity consumption of the datacenter is presented on monthly basis and electricity demand of datacenter for each cooling system is listed on annual basis. Developed software is validated by using power demand value, physical properties of datacenter building information and equipment list of an existing data center. Typical climate data insertion tool and economic analyses will be added to the software in order to be used worldwide. Therefore, this is an ongoing study.
FPGA Based Chaotic Cryptosystem

This paper describes the encryption and decryption scheme of a RGB image using Field Programmable Gate Arrays (FPGA). Logistic map (one dimensional) is used for the chaotic sequence generation and synchronization between driver and driven system using nonlinear control law. Under the synchronized situation, the pixel data of the plain RGB image is encrypted in driver side and send to driven side. After receiving this encrypted data in driven and using the inverse method it decrypts and store in its memory and finally produce the original RGB image. The projected method is verified over various RGB images and the results approve the algorithm validity.
SVD-Based Factored-Form Extended Kalman Filters for State Estimation in Nonlinear Continuous-Discrete Stochastic Systems

Thisresearch aims at presenting novel Extended Kalman Filtering (EKF) state estimation tools intended for treating nonlinear continuous-discrete stochastic scenarios, whose process model is simulated by an Itô-type Stochastic Differentiat Equation (SDE) and measurement model has a discrete-time fashion. Our investigation employs the so-called “discrete-discrete” approach, which is grounded in SDE discretization schemes. In particular, it presents EKF-based state estimation algorithms grounded in the Euler-Maruyama and Itô-Taylor discretizations of the strong convergence orders 0.5 and 1.5. The novelty of these methods stems from the idea of using the Singular Value Decomposition (SVD) for evolution of the mean and covariance in their time- and measurement-update steps. In other words, instead of the full covariance matrices, their SVD factors are computed and propagated, only. This raises numerical robustness of the EKF technique and allows the information matrix to be easily available within such a state estimation run. Our novel filters are justified theoretically and examined and compared numerically to their classical EKF predecessors in an aircraft coordinated turn scenario with ill-conditioned measurements.
Fast Omnidirectional Depth Densification

Omnidirectional cameras are commonly equipped with fisheye lenses to capture 360-degree visual information, and severe spherical projective distortion occurs when a 360-degree image is stored as a two-dimensional image array. As a consequence, traditional depth estimation methods are not directly applicable to omnidirectional cameras. Dense depth estimation for omnidirectional imaging has been achieved by applying several offline processes, such as patch-matching, optical flow, and convolutional propagation filtering, resulting in additional heavy computation. No dense depth estimation for real-time applications is available yet. In response, we propose an efficient depth densification method designed for omnidirectional imaging to achieve 360-degree dense depth video with an omnidirectional camera. First, we compute the sparse depth estimates using a conventional simultaneous localization and mapping (SLAM) method, and then use these estimates as input to a depth densification method. We propose a novel densification method using the spherical pull-push method by devising a joint spherical pyramid for color and depth, based on multi-level icosahedron subdivision surfaces. This allows us to propagate the sparse depth continuously over 360-degree angles efficiently in an edge-aware manner. The results demonstrate that our real-time densification method is comparable to state-of-the-art offline methods in terms of per-pixel depth accuracy. Combining our depth densification with a conventional SLAM allows us to capture real-time 360-degree RGB-D video with a single omnidirectional camera.
Backtalk: The emerging science of test-taking disengagement

When students take tests, their performance is a result not just of their academic achievement but of their engagement in the actual test. Rapid guessing is a sign that students aren’t fully engaged in the test but are instead rapidly filling in answers without regard to whether they are correct. Steven Wise explains that computer-based testing allows those interpreting test results to see when students engaged in rapid guessing. It’s also possible for testing programs to alert the test proctor or student to rapid guessing. He recommends that these tools become a routine part of student testing.
Rerandomizable Signatures Under Standard Assumption

The Camenisch-Lysyanskaya rerandomizable signature (CL-RRS) scheme is an important tool in the construction of privacy preserving protocols. One of the limitations of CL-RRS is that the signature size is linear in the number of messages to be signed. In 2016, Pointcheval-Sanders introduced a variant of rerandomizable signature (PS-RRS) scheme which removes the above limitation. However, the security of PS-RRS scheme was proved under an interactive assumption. In 2018, Pointcheval-Sanders improved this to give a reduction under a parameterized assumption. In 2012, Gerbush et al. introduced the dual-form signature technique to remove the dependency on interactive/parameterized assumption. They applied this technique on the CL-RRS scheme (for single message) and proved its unforgeability under static assumptions instead of the interactive assumption used in the original work but in the symmetric composite-order pairing setting. In this work, we realize a fully rerandomizable signature scheme in the prime order setting without random oracle based on the SXDH assumption. The signature structure is derived from Ghadafi’s structure-preserving signature. We first apply the dual-form signature technique to obtain a composite-order variant, called RRSc. A signature in RRSc consists of only two group elements and is thus independent of the message block length. The security of the proposed scheme is based on subgroup hiding assumptions. Then we use the dual pairing vector space framework to obtain a prime-order variant called RRS and prove its security under the SXDH assumption.
Fast and Efficient Implementation of Lightweight Crypto Algorithm PRESENT on FPGA through Processor Instruction Set Extension

As Internet of Things (IoT) technology becomes widespread, the importance of information security increases. PRESENT algorithm is a major lightweight symmetric-key encryption algorithm for IoT devices. Compared to the Advanced Encryption Standard (AES), PRESENT uses a lower amount of resources while achieving the same level of security. In this paper, we implement PRESENT with different design methodologies including hand-coded RTL, Vivado HLS, PicoBlaze, VerySimpleCPU (VSCPU) based microcontrollers, and a customized VSCPU. The customized VSCPU design is based on optimizing the instruction set architecture for the algorithm specifics of PRESENT. Our results show that the customized VSCPU design metholodogy can be more efficient than HLS and PicoBlaze while providing the flexibility compared to RTL designs.