Reviewerly/exHarmony huggingface dataset:
Digital forensics cloud log unification: Implementing CADF in Apache CloudStack

Cloud computing is an important step in our era, delivering many advantages in business and our daily life. However, as every new technology, various challenges are brought into light with one of them being the misuse of Cloud computing environments for criminal activities. As such, Cloud service providers have to establish adequate forensic capabilities in order to support forensics investigations in the event of illegal activities in the cloud. In order to help forensics investigations, this paper deals with log format unification in cloud platforms using Distributed Management Task Force's (DMTF) Cloud Auditing Data Federation (CADF) standard. CADF event logging is utilised in the widely used OpenStack, and we have modified the Apache CloudStack platform to become forensically sound. Furthermore, we investigated the existing CloudStack platform along with the proposed CADF event model implemented, with regards to the principles of the Association of Chief Police Officers (ACPO) on handling digital evidence. The results are provided in this paper as well as an automated parsing tool/CADF event consumer, named C.Lo.D, which is freely available and can be downloaded from Github.
Speech-Driven End-to-End Language Discrimination toward Chinese Dialects

Language discrimination among similar languages, varieties, and dialects is a challenging natural language processing task. The traditional text-driven focus leads to poor results. In this article, we explore the effectiveness of speech-driven features toward language discrimination among Chinese dialects. First, we systematically explore the appropriateness of speech-driven MFCC features toward CNN-based language discrimination. Then, we design an end-to-end speech recognition model based on HMM-DNN to predict Chinese dialect words. We adopt attention mechanism to extract the discriminative words related to different Chinese dialects. Finally, through a CNN, we combine the word-level embedding and the MFCC-based features. Evaluation of two benchmark Chinese dialect corpora shows the appropriateness and effectiveness of the proposed speech-driven approach to fine-grained Chinese dialect discrimination compared to the state-of-the-art methods.
Sequence Diagrams Tailored for Software Design used to Build a Carpooling Management System

The problem of making novice software developers productive in a short period of time is important. We present a compact rule set to be used when writing sequence diagrams at the design phase of a software system development. By using sequence diagrams constructed with these rules, we intend to make programmers completely understand the software design and concentrate in the task of implementation of the modules, avoiding the need to make them think on design issues and also assuring that their implementation will comply with the requirements. Using as an example the development of a web application, we show how the rules that we propose here should be applied in sequence diagrams. Finally, we show evidence of the successful implementation of the software system by novice programmers and discuss the advantages that the proposed rule set.
Probabilistic Approaches to Detect Blocking States in Intelligent Tutoring System

A blocking state is a measurable state on an intelligent tutoring systems' user interface, which mirrors a student's cognitive state where she/he cannot temporarily make any progress toward finding a solution to a problem. In this paper, we present the development of four probabilistic models to detect a blocking state of students while they are solving a Canadian high school-level problem in Euclidean geometry on an ITS. Our methodology includes experimentation with a modified version of QED-Tutrix, an ITS, which we used to gather labelled datasets composed of sequences of mouse and keyboard actions. We developed four predicting models: an action-frequency model, a subsequence-detection model, a 1D convolutional neural network model and a hybrid model. The hybrid model outperforms the others with a $$F_1$$ score of 80.4% on the classification of blocking state on validation set while performing 77.3% on the test set.
Securing and Trustworthy Blockchain-based Multi-Tenant Cloud Computing

Multi-tenant cloud computing is crucial in cloud service providers because they could gain benefit from allocating many users or tenants with the small amount of physical resource. However, multi-tenant could also cause some serious damaged due to the same location which tenant are placed. One of the problems is data leakage performing through co-resident attack from malicious tenant which result in the data to be stolen and caused business to loss the profit. Malicious tenants could modify their request or transaction for preventing anyone to trace back to them. After studying from related paper which show some implementation of Blockchain and cloud computing, we decide to use Blockchain to support cloud computing system. In order to observe and investigate in bad activity without any modification of activities, we introduce Blockchain which will help in auditing of transactions between tenants and cloud provider any provide strong integrity of transactions log. Moreover, we also integrate the Smart Contract which help on simulation on the allocation mechanism, policy checking and other functions between cloud provider and tenants for easily integrating cloud computing system on Blockchain. Our expected result would be that cloud computing could be implemented on Blockchain together with other functional features of cloud computing.
Development of a score-to-likelihood ratio model for facial recognition using authentic criminalistic data

Automated face matching systems have emerged as a useful tool for identification purposes in criminal investigations. In a forensic context it is desirable to evaluate the findings from such comparisons as probabilities in terms of a likelihood ratio. When comparing two biometric samples, many facial recognition systems produce a score value as the output. The score describes the relative similarity between the two facial images. To obtain the likelihood ratio, it is necessary to construct a statistical model for score-to-likelihood ratio conversion. The model is highly dependent on the available training data and ideally it should reflect the relevant population as closely as possible. In order to construct a general model applicable on a national level, we use data from a national mugshot database as training data. In a full crossmatch drawing from 51563 records, we develop and evaluate five different models in a Bayesian statistical framework using a total of 9000 facial comparisons with equal distribution between same source and different source scores.
A Comparative Study of Location Privacy Preservation in the Internet of Things

Location-Based Services (LBS) can gather the location of the user. In this process, the location of the user is stored on different systems, which makes it vulnerable. In this piece of work, we have compared simple location obfuscation mechanism and Enhanced Semantic Obfuscation technique (ESOT) for ensuring location privacy. The results indicate that Simple Obfuscation Techniques have less computational overhead as compared to ESOT. While there is a tradeoff between ensuring privacy and the computational overhead, we propose a novel obfuscation technique for ensuring location privacy.
Can the E-Z Reader Model Predict Eye Movements Over Code? Towards a Model of Eye Movements Over Source Code

Studies of eye movements during source code reading have supported the idea that reading source code differs fundamentally from reading natural text. The paper analyzed an existing data set of natural language and source code eye movement data using the E-Z reader model of eye movement control. The results show that the E-Z reader model can be used with natural text and with source code where it provides good predictions of eye movement duration. This result is confirmed by comparing model predictions to eye-movement data from this experiment and calculating the correlation score for each metric. Finally, it was found that gaze duration is influenced by token frequency in code and in natural text. The frequency effect is less pronounced on first fixation duration and single fixation duration. An eye movement control model for source code reading may open the door for tools in education and the industry to enhance program comprehension.
Active Contours in the Complex Domain for Salient Object Detection

The combination of active contour models (ACMs) for both contour and salient object detection is an attractive approach for researchers in image segmentation. Existing active contour models fail when improper initialization is performed. We propose a novel active contour model with salience detection in the complex domain to address this issue. First, the input image is converted to the complex domain. The complex transformation gives salience cue. In addition, it is well suited for cyclic objects and it speeds up the iteration of the active contour. During the process, we utilize a low-pass filter that lets the low spatial frequencies pass, while attenuating, or completely blocking, the high spatial frequencies to reduce the random noise connected with favorable or higher frequencies. Furthermore, the model introduces a force function in the complex domain that dynamically shrinks a contour when it is outside of the object of interest and expands it when the contour is inside the object. Comprehensive tests on both synthetic images and natural images show that our proposed algorithm produces accurate salience results that are close to the ground truth. At the same time, it eliminates re-initialization and, thus, reduces the execution time.
Multilevel Reversible Data Hiding Based on Difference Histogram for 3D Point Cloud Models

This paper proposes a novel reversible data hiding method for 3D point cloud models. The difference histogram is constructed by calculating the coordinate differences between each two points which makes the difference histogram more concentrated. A multilevel histogram shifting mechanism is used in order to enlarge the embedding capacity. Experimental results validate the effectiveness of our method and demonstrate that it can achieve better performance compared with the previous related methods.
Genetic Algorithm Based Pick and Place Sequence Optimization for a Color and Size Sorting Delta Robot

Delta Robots are used in industry for light weight material handling and sorting. This paper presents a sequence optimizing methodology for a color and size sorting delta robot. It finds the optimum path in the task space to perform an industry emulated scenario. An OpenCV-Python program was developed to sort objects according to their colors and sizes. The static positional coordinates of the objects in the robot workspace are obtained using the program. Genetic algorithm is used for pick-and-place sequence optimization to ensure that the sorting process is performed in the shortest possible path. The static positional coordinates are used to calculate the fitness. Single point crossover and mutation are applied with elitist selection when the current generation evolves to the next generation. The genetic algorithm ensures that the sequence of pick-and-place converges to the highest fitness in minimum number of generations reducing the computational time.
The Differential Benefits of Cloud Computing for Small and Medium Versus Large Firms

Cloud computing is a fundamental shift in the way computing power is acquired and consumed. It is a special case of IS outsourcing where some IT-related tasks and resources are outsourced to cloud vendors. Cloud computing confers unique capabilities such as heterogeneity, scalability, accessibility, availability, consumption based pricing, fully managed and standardized services on the adopting organizations. These capabilities in-turn result in several strategic and operational benefits to the adopting organizations. However, these capabilities are not leveraged by all adopting firms uniformly. Firms utilize these capabilities based on their orientation towards exploration and exploitation. While SMEs are more aligned towards exploratory activities, large enterprises are inclined towards exploitation. Therefore, we posit that the SMEs and large enterprises accrue differential benefits from cloud adoption such that SMEs leverage cloud to attain strategic benefits and large enterprises seek operational benefits from cloud adoption. Through a survey based exploratory study we found that there is a systematic difference between SMEs and large enterprises in the way they leverage cloud capabilities. Based on the results, we deduce that managers of SMEs believe that they can use cloud computing for exploration and on the contrary, managers of large enterprises believe that they can use cloud computing for exploitation.
Tabellion

A legal contract is an agreement between two or more parties as to something that is to be done in the future. Forming contracts electronically is desirable since it is convenient. However, existing electronic contract platforms have a critical shortcoming. They do not provide strong evidence that a contract has been legally and validly created. More specifically, they do not provide strong evidence that an electronic signature is authentic, that there was mutual assent, and that the parties had an opportunity to read the contract. We present Tabellion, a system for forming legal contracts on mobile devices, such as smartphones and tablets, that addresses the above shortcoming. We define four secure primitives and use them in Tabellion to introduce self-evident contracts, the validity of which can be verified by independent inspectors. We show how these primitives can be implemented securely in the Trusted Execution Environment (TEE) of mobile devices as well as a secure enclave in a centralized server, all with a small Trusted Computing Base (TCB). Moreover, we demonstrate that it is feasible to build a fully functional contract platform on top of these primitives. We develop ~15,000 lines of code (LoC) for our prototype, only ~1,000 of which need to be trusted. Through analysis, prototype measurements, and a 30-person user study, we show that Tabellion is secure, achieves acceptable performance, and provides slightly better usability than the state-of-the-art electronic contract platform, DocuSign, for viewing and signing contracts.
Best Polynomial Harmony Search with Best <i>β</i>-Hill Climbing Algorithm

Abstract Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory consideration, replace the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β -hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β -hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β -hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHS β –HC and the second one is called Imp. PHS β –HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHS β –HC) and 3rd (PHS β –HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHS β –HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHS β –HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHS β –HC algorithm produces the best results for three test functions, and the proposed Imp. PHS β –HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future.
Investigation of Acoustic Features for Voice Activation Problem

In this paper we examine the results of using different acoustic feature computation pipelines for classifying audio keywords with a convolutional neural network (CNN). We compare the use of Mel-frequency cepstral coefficients (MFCCs) and a simple filterbank averaging technique. Also we examined the influence of MFCCs computation parameters on the resulting quality. The results show that CNNs benifit from using prior knowledge in acoustic feature computation. In our experiments we got 30% drop in accuracy while switching from MFCC to filterbank averaging. Furthemore, the default values of MFCCs parameters that are used in many libraries might not be the best for voice activation problem: frame length of 55 ms showed better results than default length of 20 ms.
Retro-Remote Sensing With Doc2Vec Encoding

In this work, we attempt to address the issue of developing a sophisticated text encoder for retro-remote sensing application. The encoder converts ancient landscape descriptions into a fixed-size vector that, adequately, represents the available information. This vector is then used as a conditioning data to a Generative adversarial network (GAN) that synthesizes the equivalent image. We propose using a pre-trained Doc2Vec encoder for text encoding and train a Wasserstein GAN (a variant of GAN) to convert landscape descriptions written by travelers and geographers into the equivalent image. Qualitative and quantitative analysis of the generated images signify usefulness of the proposed method.
Energy-Efficient Stable and Balanced Task Scheduling in Data Centers

It is well known that load balancing in data centres can lead to unnecessary energy usage if all servers are kept active. Using dynamic server provisioning, the number of servers that serve requests can be reduced by turning off idle servers and thereby saving energy. However, such a scheme, usually increases the risk of instability of server queues. In this work, we analyze the trade-off between energy usage and stability of servers in a data center when we balance the load by dispatching arriving jobs. We propose algorithms to solve a stability and energy objective stochastic optimization problem with a high degree of flexibility to handle the trade-off between these two objectives. We consider variable size jobs to apply load balancing on selected active servers and find that the optimal solution is an NP-hard problem. We therefore develop two computationally efficient greedy and randomized approximation schemes to achieve the trade-off between these objectives. We investigate the performance of our proposed algorithms in minimizing the risk of queue length growth as well as the number of active servers needed to serve jobs, and compare it with several metrics in heterogeneous load scenarios.
Smart Rent Portal using Recommendation System Visualized by Augmented Reality

Context: Locating products or services on-line that meet users want is more and more troublesome thanks to the massive pool of decisions to think about before inbound at the required one. A user might pay a substantial quantity of your time exploring varied on-line resources to find things that work his needs. Moreover, users might not forever categorical their preferences in an exceedingly manner that simply matches them to things that might meet them. Objective: Searching out things on-line has been done principally through information queries that come back an inventory of the foremost appropriate things. Recommendation systems technology may be applied to ease the task of locating the desired things on-line. Method: This study proposes a recommender system allows that permits that allow users to hold out a preference-based cooperative filtering search on rental properties and enables them to refine those preferences based on shallow learning. The room also is visualized using AR and Vuforia. Results: This recommendation approach has been shown to supply additional correct search results. The system was developed as an Internet application victimization Handlebars for front-end and Nodes- ExpressJs for Back-end. Conclusion: The system performs better than existing algorithms and predicts better in memory-based approach.
Sentiment Analysis on Motor Vehicles Amendment Act, 2019 an Initiative by Government of India to follow traffic rule

With the new declaration of Motor Vehicles Amendment Act, 2019 in India the penalty of breaking traffic rule is going to reduce in India. Breaking the traffic rule causes an unwanted accident in the country. For the safety of people Government Of India has taken a serious step to follow traffic rule with heavy penalty who breaks it. But as the new rule applied there is a tremendous unwanted issue among the traffic police and public. This paper is written to understand the sentiment of the public which is face due to new traffic rules based on Twitter data. This paper aimed at reviewing the general implication of the Motor Vehicle Amendment Act, 2019 on the people of India.
Automatic gate control for highly secure organization using RFID and GSM Technology

The main objective of this research is to the intimate fake person entering into a highly secured organization like Hospitals, Schools, Industries and Colleges using RFID and GSM Technology. Traditionally, most of the people used to enter into secured places to steal or create unwanted problems which violating the terms and condition of the organization. In this regard, the proposed research article claims to forward fake person to the corresponding or authorized person in the same instance of time. Thereby, the accomplishment of the aforesaid security system is maintained by RFID Tag-based database by the organization. A novelty approach restricts unauthorized people from getting into the secured organization or zone. The suggested module in this proposed research works more efficiently which suits real-time execution.
From real to virtual: nature tourism insights

There is a fruitful ground for technologies such as augmented and virtual reality to be adopted by the service industry in order to further develop and enhance the unique experience of consumers. The tourism industry (with special reference to nature tourism) is one of the sectors that can very well evolve such technologies in their products and services. Such adoption will increase the engagement towards the destinations and the enterprises. In parallel such technologies can further increase the competitiveness and product and service quality. However, despite the substantial amount of beneficial impacts of such technological adoption, there is a lack of applications specialised in nature tourism. This article provides specific examples and practices suggesting the application of Virtual and Augmented Realities to nature tourism. The study also progresses to the provision of conceptual evidence in order to empower the extended technologies to nature tourism, whilst reducing the hesitance of technological adaptation.