Reviewerly/exHarmony huggingface dataset:
Review of Credit Card Fraud Detection Techniques

Credit card fraud is one of the most prevalent issues of this century. Its detection and prevention is of the utmost importance to various banks and other financial institutions. This paper discusses a plethora of detection techniques ranging from Genetic algorithms to Sequence Matching to Neural Networks. These are among the few techniques discussed, analyzed and reviewed below. This paper also discusses various data manipulation techniques used to prepare data for the aforementioned detection techniques.
Structured Stochastic Recurrent Network for Linguistic Video Prediction

Intelligent machines are expected to have the capability of predicting impending occurrences. Inspired by video frame prediction and video captioning, we introduce a new task of Linguistic Video Prediction (LVP), which aims to predict the forthcoming events based on past video content and generate corresponding linguistic descriptions. Different from traditional video captioning that describes one specifically happened event, LVP is an open task involving one-to-many mappings between past and future. It explores different visual clues and associates them with potential events to generate corresponding descriptions. To address this task, we propose an end-to-end probabilistic approach named structured stochastic recurrent network (SRN) to characterize the one-to-many connections between past visual clues and possible future events. Specially, we first propose hierarchical-structured latent variables to represent the choice of event theme. Second, we introduce a stochastic attention module to capture the variations of the focused visual clues. Given a video, our model is able to generate multiple linguistic predictions by focusing on different event themes and visual clues. Experiments on ActivityNet dataset showed that the proposed model not only yields more informative predictions measured by BLEU, METEOR, ROUGE-L, CIDEr and SPICE scores, but also generates significantly more diverse predictions with higher recall rates to correctly hit the ground-truth.
A Radical-Based Method for Chinese Named Entity Recognition

Chinese characters are composed of radicals, and their radicals have the distinction between "shaped parts" (representing semantics) and "sound parts" (representing speech). As a hieroglyph, many radicals of Chinese characters have certain semantic information, which can effectively improve the performance of Chinese named entity recognition. In the Chinese named entity recognition, many related studies use Bi-LSTM to extract the semantic features from radicals. However, the LSTM-based model cannot effectively extract the semantic information of radicals due to ambiguity in partitioning the granularity of radicals and weak dependency between Chinese radicals. Therefore, this paper presents a radical neural network method RCBC (Radical CNN-BiLSTM-CRF). The experimental results on SIGHAN 2006 Bakeoff MSRA dataset and Peking University's People's Daily dataset in 1998 indicate that this model can effectively extract the semantic information of Chinese radicals and improve the performanceof Chinese named entity recognition compared with the traditional model.
Text Recognition on Images from Social Media

Text recognition problem has been studied many years. A few OCR engines exist, which successfully solve the problem for many languages. But these engines work well only with high quality scanned images. Social networks nowadays contain large number of images that need to analyze and recognize the text contained in them, but they have different quality: mixed text with images, poor quality images taken from camera of smartphone, etc. In this paper a text extraction pipeline is provided to address text extraction from various quality images collected form social media. Input images are categorized into different classes and then class specific preprocessing is applied to them (illumination improvement, text localization etc.). Then OCR engine used to recognize text. In the paper we present results of our experiments on dataset collected from social media.
Exploiting Attention for Visual Relationship Detection

Visual relationship detection targets on predicting categories of predicates and object pairs, and also locating the object pairs. Recognizing the relationships between individual objects is important for describing visual scenes in static images. In this paper, we propose a novel end-to-end framework on the visual relationship detection task. First, we design a spatial attention model for specializing predicate features. Compared to a normal ROI-pooling layer, this structure significantly improves Predicate Classification performance. Second, for extracting relative spatial configuration, we propose to map simple geometric representations to a high dimension, which boosts relationship detection accuracy. Third, we implement a feature embedding model with a bi-directional RNN which considers subject, predicate and object as a time sequence. We evaluate our method on three tasks. The experiments demonstrate that our method achieves competitive results compared to state-of-the-art methods.
Co-scheduler: Accelerating Data-Parallel Jobs in Datacenter Networks with Optical Circuit Switching

The optical circuit switch (OCS) in recently proposed hybrid electrical/optical datacenter networks (Hybrid-DCN) can only be used to transfer large flows (i.e., flows with a large size of data). Current job schedulers for data-parallel frameworks are not suitable for Hybrid-DCN, since they neither place tasks to aggregate data traffic to take advantage of OCS nor schedule tasks to minimize the Coflow completion time (CCT). In this paper, we propose Co-scheduler, a job scheduler for dataparallel frameworks that aims to improve job performance by attempting to place the tasks of a job to aggregate enough data traffic to take advantage of OCS and minimize the CCT in Hybrid-DCN. Specifically, for each job, Co-scheduler computes a guideline on the number of racks to place the job's input data and to run the job's map tasks, so that the job can potentially take full advantage of OCS to transfer its data. When the map tasks of a job complete, Co-scheduler computes all the possible schedules of the reduce tasks of the job. Each possible schedule includes the number of racks to schedule the reduce tasks that enables the job to use OCS to transfer its data, and the number of reduce tasks to place on each of the racks that minimizes CCT of the job. Next, Co-scheduler selects a best schedule among all the possible schedules so that the job completion time is minimized. Finally, Co-scheduler schedules the map tasks and reduce tasks of the job based on the computed guideline and best schedule. The evaluation demonstrates that compared to the state-of-theart schedulers, Co-scheduler achieves performance improvements on makespan, average job completion time, and average CCT by up to 51.2%, 54.6% and 73.6%, respectively.
Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

Latent dirichlet allocation (LDA) is a representative topic model to extract keywords related to latent topics embedded in a document set. Despite its effectiveness in finding underlying topics in documents, the traditional algorithm of LDA does not have a process to reflect sentimental meanings in text for topic extraction. Focusing on this issue, this study aims to investigate the usability of both LDA and sentiment analysis (SA) algorithms based on the affective level of text. This study defines the affective level of a given set of paragraphs and attempts to analyze the perceived trust of the methodologies in regards to usability. In our experiments, the text of the college scholastic ability test was selected as the set of evaluation paragraphs, and the affective level of the paragraphs was manipulated into three levels (low, medium, and high) as an independent variable. The LDA algorithm was used to extract the keywords of the paragraph, while SA was used to identify the positive or negative mood of the extracted subject word. In addition, the perceived trust score of the algorithm was evaluated by the subjects, and this study verifies whether there is a difference in the score according to the affective levels of the paragraphs. The results show that paragraphs with low affect lead to the high perceived trust of LDA from the participants. However, the perceived trust of SA does not show a statistically significant difference between the affect levels. The findings from this study indicate that LDA is more effective to find topics in text that mainly contains objective information.
Investigating the Impact of Development Task on External Quality in Test-Driven Development: An Industry Experiment

Reviews on test-driven development (TDD) studies suggest that the conflicting results reported in the literature are due to unobserved factors, such as the tasks used in the experiments, and highlight that there are very few industry experiments conducted with professionals. The goal of this study is to investigate the impact of a new factor, the chosen <i>task</i> , and the <i>development approach</i> on external quality in an industrial experimental setting with 17 professionals. The participants are junior to senior developers in programming with Java, beginner to novice in unit testing, JUnit, and they have no prior experience in TDD. The experimental design is a <inline-formula><tex-math notation="LaTeX">$2\times 2$</tex-math></inline-formula> cross-over, i.e., we use two tasks for each of the two approaches, namely TDD and incremental test-last development (ITLD). Our results reveal that both <i>development approach</i> and <i>task</i> are significant factors with regards to the external quality achieved by the participants. More specifically, the participants produce higher quality code during ITLD in which splitting user stories into subtasks, coding, and testing activities are followed, compared to TDD. The results also indicate that the participants produce higher quality code during the implementation of Bowling Score Keeper, compared to that of Mars Rover API, although they perceived both tasks as of similar complexity. An interaction between the <i>development approach</i> and <i>task</i> could not be observed in this experiment. We conclude that variables that have not been explored so often, such as the extent to which the task is specified in terms of smaller subtasks, and developers’ unit testing experience might be critical factors in TDD experiments. The real-world appliance of TDD and its implications on external quality still remain to be challenging unless these uncontrolled and unconsidered factors are further investigated by researchers in both academic and industrial settings.
The AtLarge Vision on the Design of Distributed Systems and Ecosystems

High-quality designs of distributed systems and services are essential for our digital economy and society. Threatening to slow down the stream of working designs, we identify the mounting pressure of scale and complexity of (eco-)systems, of ill-defined and wicked problems, and of unclear processes, methods, and tools. We envision design itself as a core research topic in distributed systems, to understand and improve the science and practice of distributed (eco-)system design. Toward this vision, we propose the AtLarge design framework, accompanied by a set of 8 core design principles. We also propose 10 key challenges, which we hope the community can address in the following 5 years. In our experience so far, the proposed framework and principles are practical, and lead to pragmatic and innovative designs for large-scale distributed systems.
Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks

Footwear outsoles acquire characteristics unique to the individual wearing them over time. Forensic scientists largely rely on their skills and knowledge-gained through years of experience-to analyse such characteristics on a shoeprint. In this work, we present a deep learning model that, for the first time, can predict the wear pattern on a unique dataset of shoeprints that captures the life and wear of a pair of shoes. We also present an additional architecture able to reconstruct the outsole back to its original state on a given week, and provide empirical evaluations of the performance of both models.
iStar4RationalAgents: Modeling Requirements of Multi-agent Systems with Rational Agents

Multi-agent systems (MAS) involve a wide variety of agents that interact with each other to achieve their goals. Usually, the agents in a MAS can be reactive or proactive, this choice defines the rationale of its elements. Rational Agents is the term used to mention a set of four kinds of reactive and proactive agents. Conceptual models which represent the rational agents’ intentionality can be used to design and analyze MAS in a systematic and structured manner. Conceptual modelling can be used to uncover mistakes and gaps in reasoning that are missed or obscured via ad hoc evaluation. However, the modelling of MAS with different rational agents is a non-trivial task, due to the specificity of their domain concepts, also at requirements level. This paper presents an approach to model MAS with rational agents in requirements level using iStar. This is part of a Model-Driven Development approach which has been proposed to support the development of MAS with rational agents involving requirements, architecture, code and test. We extended iStar to support the modelling of main concepts of this domain in a systematic way based on a process to conduct iStar extensions. We modelled a MAS to validate and illustrate the usage of our extension and evaluate the results using a survey with experienced researchers/developers in MAS.
An Improved Reversible Data Hiding Scheme Through Novel Encryption

Reversible data hiding is an active research area in the field of medical image transmission. In general, along with the medical image, we may want to transmit some additional data such as patient details, diagnosis results, etc. The reversible data hiding schemes allow us to embed such additional information in the medical image itself instead of sending it as a separate file. A reversible data hiding through encryption scheme is already proposed and the existing scheme achieved to obtain good embedding rate with very less bit error rate. In this paper, we modified the existing reversible data hiding through a novel encryption process to improve the embedding rate by a factor of three without compromising the bit error rate. As per the proposed scheme, three bits of information could be inserted into a single image block of size B × B pixels. Similar to the existing scheme, in the proposed scheme also the sender and receiver want to share three encryption/decryption keys only.
BayMax: A Smart Healthcare System Provide Services to Millennials Using Machine Learning Technique

Health is the utmost importance to any human being. To take care of oneself in this busty world we need some assistance. The world is revolving with smart technologies so we need a smart health care assistance that tracks one's activities, moods and suggest precautious actions when required. The primary objective of this product is to be time efficient along with being as an easily accesses able personal health assistant. To build a cost efficient personal assistance with good quality, monitoring of the messages from user is necessary. At present they are managed in an impromptu way. The paper will put forward the use of a Personal assistance chatbot to handle the messages from users and collectively provide the appropriate answers to it. A model has been proposed to enumerate precautions for the health related issues.
A Method of Classifying Japanese Sign Language using Gathered Image Generation and Convolutional Neural Networks

This paper proposes a method for classifying Japanese sign language (JSL) using a combined gathered image generation technique and a convolutional neural network (CNN) approach. In the combined gathered image generation, the maximum difference from the previous and next images is calculated for each block, and the block information that had maximum difference was embedded into an image on all blocks. After information on all images has been gathered into single words, the CNNs are used to extract features for the classification of JSL words. A multi-class support vector machine (SVM) is then used to classify words related to greeting and requesting. The mean and the standard deviation of the recognition accuracy of the proposed method were experimentally shown to be 84.2% and 4%, respectively. These results suggest that it is possible to obtain information for classifying 10 JSL words using the proposed combined gathered image generation and CNN approach.
SqlSol: An accurate SQL Query Synthesizer

SQL is the programming language for communicating with relational databases, but writing SQL queries is challenging for many end users due to lack of programming knowledge. In this paper, we present an efficient and accurate algorithm that helps users to synthesize SQL queries from IO examples, which is the first algorithm to encode SQL synthesis problem into constraint-solving problem. We propose an axiom that encodes the semantics of a SQL query into logic constraints, and decompose the SQL synthesis problem into two parts: problem-encoding and constraint-solving. For the problem-encoding part, we use a SQL template that is same as prior work and parameterize it, then based on this axiom, we encode the parameters into logic constraints. For the constraint-solving part, we use the off-the-shelf modern SMT solvers. Our algorithm supports multiple IO examples, therefore users can add more examples to refine the solution until a correct one is found. We implemented a tool, SqlSol, and evaluated it on 171 benchmarks. The results showed that it efficiently solved 68% of the benchmarks in 3 s in average. For those SqlSol cannot solve, SqlSol terminated in 4 s in average.
Asymmetric Information in High-Value Low-Frequency Transactions: Mitigation in Real Estate Using Blockchain

The saying 'buyer be aware' has been used as an excuse from a seller's perspective to withhold information that could negatively impact a transaction. This asymmetric information is especially prevalent in high–value, low-frequency assets. Using New Zealand real estate as an exemplar to understand the difficulties faced in such a transaction, we delve into the characteristics of the transaction and specifically the asymmetric information that is predominant in the real estate industry. Understanding the processes and stakeholders involved gives us the possibility to introduce blockchain as a system to mitigate asymmetric information. We identify the bottlenecks in the current processes and suggest possible solutions that capitalize on the blockchain characteristics.
A Probability Prediction Based Mutable Control-Flow Attestation Scheme on Embedded Platforms

Control-flow attacks cause powerful threats to the software integrity. Remote attestation for control flow is a crucial security service for ensuring the software integrity on embedded platforms. The fine-grained remote control-flow attestation with execution-profiling Control-Flow Graph (CFG) is applied to defend against control-flow attacks. It is a safe scheme but it may influence the runtime efficiency. In fact, we find out only the vulnerable parts of a program need being attested at costly fine-grained level to ensure the security, and the remaining normal parts just need a lightweight coarse-grained check to reduce the overhead. We propose Mutable Granularity Control-Flow Attestation (MGC-FA) scheme, which bases on a probabilistic model, to distinguish between the vulnerable and normal parts in the program and combine fine-grained and coarse-grained control-flow attestation schemes. MGC-FA employs the execution-profiling CFG to apply the remote control-flow attestation scheme on embedded devices. MGC-FA is implemented on Raspberry Pi with ARM TrustZone and the experimental results show its effect on balancing the relationship between runtime efficiency and control-flow security.
MFZKAP: Multi Factor Zero Knowledge Proof Authentication for Secure Service in Vehicular Cloud Computing

In vehicular cloud computing, a number of vehicle users act as nodes and communicate with each other to obtain location based services and data. Malicious users or service providers can collect the information of locations travelled and application accessed from vehicle users. Therefore, the authentication of vehicle users becomes challenge with preserving their actual identity and location privacy. In this paper we propose an efficient multi factor authentication scheme for vehicular cloud computing environment. This scheme uses zero knowledge based algorithm for user identity verification. It provides good privacy preservation, so that the adversaries are not able to succeed in tracing any vehicles. The simulation outcomes confirms that the proposed scheme achieves superior compared to existing schemes in terms of computational cost. Therefore, our scheme is appropriate for providing secure solution in vehicular cloud.
Arbitrable blind quantum computation

Blind quantum computation (of a single-server case) is a two-party cryptographic protocol which involves a quantum computation server Bob and a client Alice who wants to delegate her quantum computation to Bob without revealing her quantum algorithms and her input to and output from the algorithms. Since Bob may be truant and pretend to execute some computation, Alice wants to verify Bob's honesty on computation. To resolve this problem, the notion of the verifiability has been considered in the literature and several protocols of verifiable blind computation have been developed. Verifiable blind quantum computation enables Alice to check whether Bob is cheating or not. In addition to the above problem, another problem could arise. If Alice pretends to be a client and is actually a competitor against Bob, then she might slander Bob by fabricating his dishonesty. Therefore, if either Alice or Bob is cheating, then a "neutral" referee other than Alice and Bob should judge which is honest. The standard definition of the verifiability guarantees that only Alice can verify Bob's computation, and thus, it should be called private verifiability. If Bob claims his innocence though he is actually cheating, then Alice cannot persuade any others that Bob is really cheating while Alice can recognize Bob's cheating. In this paper, we incorporate arbitrators as the third party into blind quantum computation to resolve the above problems and give an arbitrable blind quantum computation scheme, which provides public verifiability in some sense.
Contribution to Symmetric Cryptography by Convolutional Neural Networks

This paper presents an implementation of Convolutional Neural Network (CNN) symmetric encryption. The results based on selected criteria are compared with Advanced Encryption Standard (AES). Authors are testing both implementations using the set of differently sized and content independent files. Compared metrics include the execution time, memory occupancy and processor load. Robustness of CNN encryption against the crypto attacks was not tested and it is out of the scope of this paper. Concluded results offer the usability prospects of selected neural network for symmetric cryptographic purposes. The results of the experiments have shown that CNN has the potential for cryptographic purposes under the conditions specified in this paper.