Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning based Elasticity-compatible Heterogeneous Resource Management for Time-critical Computing

Rapidly generated data and the amount magnitude of data analytical jobs pose great pressure to the underlying computing facilities. A distributed multi-cluster computing environment such as a hybrid cloud consequently raises its necessity due to its advantages in adapting geographically distributed and potentially cloud-based computing resources. Different clusters forming such an environment could be heterogeneous and may be resource-elastic as well. From analytical perspective, in accordance with increasing needs on streaming applications and timely analytical demands, many data analytical jobs nowadays are time-critical in terms of their temporal urgency. And the overall workload of the computing environment can be hybrid to contain both time-critical and general applications. These all call for an efficient resource management approach capable to apprehend both computing environment and application features.
Iterative Framework and Privacy Preservation in Reciprocal Recommendation

Although there are many reciprocal recommenders based on different strategies which have found applications in different domains but in this paper we aim to design a common framework for both symmetric as well as asymmetric reciprocal recommendation systems (in Indian context), namely Job recommendation (asymmetric) and Online Indian matrimonial system (symmetric). The contributions of this paper is multifold: i) Iterative framework for Reciprocal Recommendation for symmetric as well as asymmetric systems. ii) Useful information extracted from explicit as well as implicit sources which were not explored in the existing system (Free-text mining in Indian Matchmaking System). iii) Considering job-seekers’ personal information like his marital status, kids, current location for suggesting recommendation. iv) Proposed Privacy preservation in the proposed framework for Reciprocal Recommendation system. These parameters are very important from practical viewpoint of a user and we have achieved improved efficiency through our framework as compared to the existing system.
Compact and Resilient Cryptographic Tools for Digital Forensics

Audit logs play a crucial role in the security of computer systems and are targeted by the attackers due to their forensic value. Digital signatures are essential tools to ensure the authentication/integrity of logs with public verifiability and nonrepudiation. Especially, forward-secure and aggregate signatures (FAS) offer compromise-resiliency and append-only features such that an active attacker compromising a computer cannot tamper or selectively delete the logs collected before the breach. Despite their high-security, existing FAS schemes can only sign a small pre-defined number (K) of logs, and their key-size/computation overhead grows linearly with K. These limitations prevent a practical adoption of FAS schemes for digital forensics. In this paper, we created new signatures named COmpact and REsilient (CORE) schemes, which are (to the best of our knowledge) the first FAS that can sign (practically) unbounded number of messages with only a sub-linear growth in the keysize/computation overhead. Central to CORE is the creation of a novel K-time signature COREKBase that has a small-constant key generation overhead and public key size. We then develop CORE-MMM that harnesses COREK Base via forward-secure transformations. We showed that CORE-MMM significantly outperforms its alternatives for essential metrics. For instance, CORE-MMM provides more than two and one magnitudes faster key updates and smaller signatures, respectively, with smaller private keys. CORE-MMM also offers extra efficiency when the same messages are signed with evolving keys. We formally prove that CORE schemes are secure. Our analysis indicates that CORE schemes are ideal tools to enhance the trustworthiness of digital forensic applications.
Steganography: Double Encrypted Image Deployed In Cloud

Embedding data into a given medium is called Steganography}, in such a way that it cannot be detected in a given image. Also, cloud computing has altered the way information is accessed, stored, and transmitted; the emphasis is in making the data more robust and keeping it hidden within a given image. The current techniques of spatial embedding do not deliver the robustness that is needed for basic compression algorithms. To produce a better method, it takes the help of the least-significant bit paradigm. With this paradigm, we are led towards the idea of transforming the embedding system from the very beginning, which is to hide the data in the domains of frequency. The following paper proposes a method of transform embedding which uses current digital encryption methods and improves the steganography results by using the empirical data, and the encrypted image is deployed in the cloud to be used from anywhere. This method is more robust than the existing spatial embedding methods. The claims are backed by the experimental results which are shown in the paper.
ResFusion: A Residual Learning Based Fusion Framework for CTR Prediction

CTR prediction tasks deal with the problem of evaluating the probability of users clicking on products, and have been widely deployed in many online recommendation and advertising platforms. Mainstream CTR models can be divided into two categories: the traditional machine learning models (e.g., GBDT [7]) that learn the linear feature combinations for prediction, and deep learning based algorithms (such as DeepFM [9]) for modeling the complex and sparse feature correlations. Some recent works proposed to fuse these two kinds of models for prediction. These fusion models either feed the intermediate results learned by one model into the second category or rely on the ensemble techniques to fuse two independently trained model outputs. In this paper, we propose a residual learning based fusion framework for CTR prediction. The key idea is that, we first train a model (e.g., GBDT), and let the second model (e.g., DeepFM) learn the residual part that can not be accurately predicted by the first model. The soundness of this framework is that: as the prediction power of these two kinds of models is complementary, it is easier to let the second model learn the residual output that can not be well captured by the first model. We show that our proposed framework is flexible and it is easier to train with faster convergence. Extensive experimental results on three real-world datasets show the effectiveness of our proposed framework.
Recognising the Types of Software Assets and Its Impact on Asset Reuse

Early software reuse success triggered immense euphoria culminating in many publications on the topic. However, documented successes could not keep pace. Some commentators even called for the end of the reuse. This paper examines what can be reused and how. Interviews indicated the need for a more precise asset definition and an enhanced asset description. It is suggested that identified types of assets should be treated individually, thus increasing their chance of reuse. This will assist in overcoming the biggest hurdle of reuse – the asset discovery. Here the so far manual asset identification/selection will be replaced by a triggered, systematic and cognitive asset suggestion via “Reuse Suggestion Engine” based on (a) sufficiently structured asset records and (b) continuously maintained asset entries. This interim research report provides business management guidelines of how both the assets as well as the organisational set-up should be shaped to be prepared for the era of automation which will propel us into a future, more abstract chapter of continuous software reuse resulting from an automated solutioning. This update will have a positive impact on the increasing number of traditional hardware products whose heart and soul is now software.
Genetic Algorithm 3D Trajectory Planning Based on Digital Map Pre-processing for Tilt-rotor Aircraft

Abstract Tilt-rotor aircraft is a new type of aircraft which can take off and land vertically and cruise at high speed. It can be used to perform specific tasks by special departments such as public security or fire rescue. The terrain environment and threat of tasks are complex and changeable. In order to ensure flight safety, the flight trajectory should be planned in advance. As a global optimization algorithm simulating biological evolution process, genetic algorithm is mostly used in two-dimensional flight path planning. In this paper, the three-dimensional digital map is preprocessed according to the actual mission environment and constraints of the tiltrotor aircraft. The related genetic operators and individual evaluation function of the genetic algorithm are improved to reduce the calculation of the trajectory planning and effectively avoid the partial optimal solution. The simulation results show that after preprocessing the 3D digital map, a 3D mission trajectory with the best cost in the complex mission environment can be successfully planned by the improved genetic algorithm.
Fast BCH 1-Bit Error Correction Combined with Fast Multi-Bit Error Detection

In this paper, an error correction is combined with additional detection using a BCH code. It is shown that a fast 1-bit correction can be efficiently combined with fast BCH multibit detection for all up to (2T-1)-bits errors, where T can be specified. In the case of an error, the syndrome is calculated and both a speculative 1-bit correction and an error detection process are started in parallel. The proposed error detection process checks if the actual error syndrome components contradict the 1-bit error assumption. The corresponding determinants for 2-bit to T-bit errors are computed in ascending order up to the first determinant indicating an error larger than 1-bit. This differs from the classic approach where a sequence of corresponding determinants containing syndrome components in descending order from T to 2 is computed. For the classic approach, the first determinant not equal 0 in descending order indicates the number of errors. The presented approach allows to speed up the detection by simplifying the determinants using the results of previous iterations. Under the assumption of a 1-bit error, the determinants can be simplified to a constant size. The proposed approach speeds up the error detection considerably. This method is useful for error detection and error correction in safety critical applications where 100 percent of the multi-bit errors up to a certain multiplicity have to be detected.
Secure and Compact Elliptic Curve LR Scalar Multiplication

Elliptic curve cryptography (ECC) can ensure an equivalent security with much smaller key sizes. Elliptic curve scalar multiplication (ECSM) is a fundamental computation used in ECC. This paper focuses on ECSM resisting simple power attack and safe error attack of side-channel attack specifically. Elliptic curve complete addition (CA) formulae can achieve secure ECSM algorithms but are inefficient from memory and computational cost perspectives. Another secure ECSM, which uses (extended) affine, is more efficient for both memory and computational costs. However, it scans input scalars from right to left. In this paper, our developed scalar multiplication algorithms also use their extended affine, but scan from left to right (LR). We also prove the security of our LR ECSM algorithms and analyze them both theoretically and experimentally. Our new LR ECSM algorithms can reduce the amount of memory by $$37.5\%$$ and reduce the computational time by more than $$40\%$$ compared to Joye’s regular 2-ary LR algorithm with CA formulae.
IoT Device Multi-layer Connection Management Mechanism with Blockchain Smart Contracts

This paper proposes a connection management mechanism for IoT devices using blockchain smart contracts. In recent years, with the development of network technology, the number of devices connected to the network is increasing exponentially as the number of devices increases, the risk of attacks increases. Therefore, blockchain plays a vital role in protecting IoT systems. In this paper, we propose and discuss a decentralized connection management mechanism to eliminate the threat associated with IoT by using blockchain’s smart contract. We proposed a multi-layer management mechanism to reduce the cost of smart contract processing costs due to a large number of IoT devices. We also proposed a tool to manage the connection of IoT devices using smart contracts. In the evaluation, we evaluated our proposed management scheme by applying two types of connection structure i.e., hub-based layer and sink-based layer. The novelty of this method is the implementation of a hierarchical management system for each layer. The effectiveness is the reduction of the smart contact processing cost of devices that disconnects and reconnects to the network.
Information System of Roof Tiles Production and Distribution

Abstract Currently, leading companies, especially in the roof tile industry, are still using manual systems in the process of production and transactions data collection activities, which made many errors and loss of data occurred. To overcome this, production and distribution information systems which aims to help and facilitate performance in producing and selling are created. The methodology used in making goods delivery information systems is Descriptive qualitative method, Object method is used as the approach, and Prototype is used as system development. The tools used in this research are Use case diagram, Scenario Diagram, Activity Diagram, Sequence Diagram, Class Diagram, Deployment Diagram, and Component Diagram. The results of this research application are made in the form of production, ordering, payment and delivery processes. This research is expected to be able to assist in providing the required information quickly and accurately, as well as able to increase the company revenue.
Analysis of Expert System Lung Disease Diagnosis System of Web-Based Disease in Cihaur Puskesmas

Abstract One of the causes of death in humans is lung disease, namely cancer. Data from the Indonesian Cancer Information &amp; Support Center (CISC) shows lung cancer is the number one cancer killer with a total of 14 percent of cancer deaths. The death rate due to lung cancer in Indonesia even reaches 88%. That must be considered to be one of human efforts in maintaining health in the lungs. Handling that can be done is to check early to be able to avoid or treat the lung disease. The existence of an expert system provides a solution to overcome this. Expert system is a computer system that stores knowledge, facts, reasoning and experience from an expert that is used to solve problems in a particular field, especially for lung disease. The purpose of this research is to design an expert system of web-based lung disease diagnosis that can help the public to carry out consultations about lung disease anywhere without having to come directly to the hospital or puskesmas/clinic. The method used is descriptive method and action. In designing the expert system, the method used is the method of object-oriented approach and prototype development method. With this expert system, it will be easier for the public to obtain information about lung disease, help the public and doctors to diagnose lung disease, and help doctors supervise the treatment of patients and the public.
How to get a Parcel surfing

This paper analyzes the phenomenon of parcel transport using moving peristaltic waves. The proposed work describes suitable test scenarios and compares the results with a suitable simulation model. The comparison shows suitable peristaltic wave parameters to successfully transport parcels. Peristaltic movement is a well-known technique from nature for good transport and locomotion and can be adapted for singulation and sortation tasks of parcels. This work answers the question of how to adapt moving peristaltic waves to achieve a parcel surfing, or even how to transport them. Performing practical test scenarios with the Peristaltic Sortation Machine (PSM) and comparing them with a suitable simulation model gives an insight into this special phenomenon. The derived DEM simulation model represents very detailed the behavior of parcels manipulated with flexible film and its actuation. Also, the DEM simulation shows a correlation between the wave setting and the parcel properties. The contributed methodology of transporting goods with moving peristaltic waves enables a successful transportation as well as the derived DEM simulation shows realistic results and is adaptable for arbitrary goods.
Sliding Window-based Regularly Frequent Patterns Mining Over Sensor Data Streams

WSNs generate a large amount of data in the form of data stream; and mining these streams with well used support metric-based sensor association rule mechanism can result in extracting interesting knowledge. Support metric-based sensor association use occurrence frequency of pattern as criteria, but the occurrence frequency of a pattern may not be an appropriate criterion for finding significant patterns. However, temporal regularity in occurrence behavior can be considered as another important measure for assessing the importance of patterns in WSNs. A frequent pattern that occurs after regular intervals in WSNs called as regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely required in real-time applications, no such algorithm has been proposed yet. Therefore, in this paper we propose a novel tree structure, called regular frequent sensor pattern stream tree (RFSPS-tree) and a new technique, called regularly frequent sensor pattern mining of data stream (RFSPMS), using sliding window-based regularly frequent sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an RFSPS-tree, our RFSPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering regularly frequent sensor patterns over sensor data stream.
Statistical Evidence for Learnable Lexical Subclasses in Japanese

It has been proposed that the Japanese lexicon can be divided into etymologically defined sublexica on phonotactic and other grounds. However, the psychological reality of this sublexical analysis has been challenged by some authors, who have appealed to putative problems with the learnability of the system. In this study, we apply a commonly used clustering method to Japanese words and show that there is robust statistical evidence for the sublexica and, thereby, that such sublexica are learnable. The model is able to recover phonotactic properties of sublexica previously discussed in the literature, and also reveals hitherto unnoticed phonotactic properties that are characteristic of sublexical membership and can serve as a basis for future experimental investigations. The proposed approach is general and based purely on phonotactic information and thus can be applied to other languages.
Security and the New Generation Workforce

A new generation workforce is seen to be changing the nature of work at a rapid pace. This new workforce differs from older generations as they are tech-savvy, prioritise flexibility and perceive their jobs as ways to reinvent themselves. In response, the idea of a ‘9-to-5’ job has been declared obsolete by many leading companies and replaced by flexible working policies and remote and virtual office environments. Both public as well as private organisations are seen to be looking for new paths to staff talent, coordinate teams and provide attractive working environments. Yet, what is often overlooked is the security implications of this new workforce. Our research found that security policies and procedures in organisations often do not include a specific approach towards the new generation workforce despite the unique set of risks that accompanies their way of working. We argue that organisations need to reconsider their security strategy to ensure that their workforce thrives in a complex world of uncertainty. By integrating security as an employee service, making security awareness experience-based and putting employee engagement at the centre, risks are not only mitigated but new opportunities for a more safe and secure workforce can be seized.
Analysis of Knowledge Management System in Pamulang University Library Based on SUMI (Software Usability Measurement Inventory)

Abstract We would like to measured the information system of Pamulang University library through the usability evaluation. The method in this study uses the adoption of the Knowledge Management System Life Cycle (KMSLC) life cycle. Where the stages used consist of Evaluation of Existing Infrastructure Analysis, Capture Knowledge, Implementing the KM system, Evaluation. The steps taken are system analysis, knowledge mapping in the application, implementation results and evaluation using usability. The questionnaire was conducted on 20 respondents, including 10 students and 10 Pamulang University lecturers. The questionnaire was conducted in 3 categories namely effectiveness, efficiency and satisfaction with 10 questions each so that there were 60 questions and 3 linkers namely 4 if they all agreed, 2 if they did not know and 0 did not agree. The results of the SUMI questionnaire against the Library are 90; 75; 75; 87.5; 90; 75. The usability evaluation results are above average, meaning that the usability in the UNPAM Library system is already in the good category.
A Strategy to Support Streaming Communication using the Intel HARPv2 Platform: A Case Study in Stereo Vision Application

The CPU-FPGA heterogeneous architectures became an attractive option for developing hardware accelerators to process computer vision algorithms. In this paper, we improve the support for streaming processing on the Intel HARPv2 platform by proposing strategies such as data ordering, double buffer, and management of multiple memory addresses. We demonstrate the feasibility of this new strategy by a case study with a hardware implementation of the Semi-Global Matching (SGM) algorithm for stereo vision. With these strategies, we can process depth images with a resolution of 1920×1080 pixels achieving a processing rate of about 48 FPS. The processing performance overcomes the state-of-art CPU-FPGA heterogeneous architectures results for processing of the promissing SGM technique.
On some problems of rule ordering in Finnish grammar

Abstract Finnish wh -movement exhibits internal roll-up movement with pied-piping and is therefore overtly successive-cyclic. On the other hand, its morphosyntax is nonlocal, suggesting countercyclic behavior. The existence of overtly cyclic computations and nonlocal agreement penetrating nearly every cyclic domain constitutes a near contradiction in this language. A solution is proposed which partially resurrects the notion of d-structure: grammatical operations are cyclic and operate in small phases (as indicated by Finnish successive cyclic wh -movement), but some operations, Agree in particular, access leftover copies of elements in situ and are not restricted by the phase impenetrability condition (PIC). PIC restricts operator/A-bar movement, not morphosyntax.