Reviewerly/exHarmony huggingface dataset:
Credit Card Transactions Data Adversarial Augmentation in the Frequency Domain

With the rapid development of e-commerce, a huge amount of trade data has been generated, which may be used by criminals for fraudulent transactions. In actual application, not all of the funds steal incidents can be found by the anomaly detection system in the bank because the system is not sensitive enough. A large amount of capital was taken by offenders every year, this makes a bad effect on the whole society. Thus, it becomes important to identify such fraudulent transactions in the massive commerce data. The advance of science and technology brings about the increase of data volume, as well as reveal the new application of machine learning, which provides researchers a way to detect vulnerability. Machine learning methods are widely used in the field of credit card fraud detection, but the imbalance between different categories poses an obstacle in the learning tasks. To alleviate this kind of issue, in this paper a model based on frequency domain characteristics be proposed, which is combined with a generative adversarial network (GAN) to augment minority class. In contrast to the traditional adversarial network, which only generates adversarial samples from the training data itself, this approach uses the frequency domain amplitude features of the data to generate diverse training data that matches the trend of data changes. Experimental results demonstrate classification performance is considerably improved and that over-fitting is alleviated when applying to multiple original datasets. Moreover, outperform other existing state-of the-art approaches.
Market reaction to large transfers on the Bitcoin blockchain - Do size and motive matter?

How does the market react to large Bitcoin transactions? We analyze effects of 2,132 transactions involving at least 500 Bitcoins. While results for all transactions are inconclusive, further analysis of transaction size and presumed transfer motives based on publicly known Bitcoin addresses of cryptocurrency exchanges reveals significant price effects depending on the type of transaction. The results indicate that the market recognizes the nature of the transfer and prices in new information.
Tempura: Query Analysis with Structural Templates

Analyzing queries from search engines and intelligent assistants is difficult. A key challenge is organizing queries into interpretable, context-preserving, representative, and flexible groups. We present structural templates, abstract queries that replace tokens with their linguistic feature forms, as a query grouping method. The templates allow analysts to create query groups with structural similarity at different granularities. We introduce Tempura, an interactive tool that lets analysts explore a query dataset with structural templates. Tempura summarizes a query dataset by selecting a representative subset of templates to show the query distribution. The tool also helps analysts navigate the template space by suggesting related templates likely to yield further explorations. Our user study shows that Tempura helps analysts examine the distribution of a query dataset, find labeling errors, and discover model error patterns and outliers.
Universal Functions Originator

Nowadays, couples of computing systems have been introduced to perform many applications, such as function approximation, pattern classification, categorization/clustering, forecasting/prediction, control, and optimization. Linear regression (LR) is commonly used for simple data where the relation between its coefficients is linear, while nonlinear regression (NLR) is used when that relation is nonlinear. Artificial neural networks (ANNs) and support vector machines (SVMs) are more efficient and they can be used for complex applications. However, each one of these approaches has its own strengths and weaknesses. This study introduces a new computing system called "universal functions originator (UFO)". This system is a new symbolic regression (SR) technique that can generate mathematical models universally through two independent optimization algorithms. Different arithmetic operators can be entered into the search pool. Also, any analytic function can be dragged into that pool. UFO has been mathematically designed and practically tested with function approximation problems. However, UFO can also be used for the applications listed above, including anomaly detection, function complication, function simplification, dimension expansion, dimension reduction, and high-dimensional function visualization. This novel computing system shows an impressive performance with many promising uses and distinct capabilities. This study reveals the mechanism of UFO and solves some numerical problems via an advanced graphical user interface (GUI) designed just to validate the process of this computing system.
Exposing Error in Poverty Management Technology: A Method for Auditing Government Benefits Screening Tools

Public benefits programs help people afford necessities like food, housing, and healthcare. In the US, such programs are means-tested: applicants must complete long forms to prove financial distress before receiving aid. Online benefits screening tools provide a gloss of such forms, advising households about their eligibility prior to completing full applications. If incorrectly implemented, screening tools may discourage qualified households from applying for benefits. Unfortunately, errors in screening tools are difficult to detect because they surface one at a time and difficult to contest because unofficial determinations do not generate a paper trail. We introduce a method for auditing such tools in four steps: 1) generate test households, 2) automatically populate screening questions with household information and retrieve determinations, 3) translate eligibility guidelines into computer code to generate ground truth determinations, and 4) identify conflicting determinations to detect errors. We illustrated our method on a real screening tool with households modeled from census data. Our method exposed major errors with corresponding examples to reproduce them. Our work provides a necessary corrective to an already arduous benefits application process.
Unified Generative Adversarial Networks for Multiple-Choice Oriented Machine Comprehension

In this article, we address the multiple-choice machine comprehension (MC) problem in natural language processing. Existing approaches for MC are usually designed for general cases; however, we specially develop a novel method for solving the multiple-choice MC problem. We take the inspiration generative adversarial networks (GANs) and first propose an adversarial framework for multiple-choice oriented MC, named McGAN . Specifically, our approach is designed as a GAN-based method that unifies both generative and discriminative MC models. Working together, the generative model focuses on predicting relevant answer given a passage (text) and a question; the discriminative model focuses on predicting their relevancy given an answer-passage-question set. Based on the competition via adversarial training in a minimize-maximize game, the proposed method takes advantages from both models. To evaluate the performance, we test our McGAN model on three well-known datasets for multiple-choice MC. Our results show that McGAN can achieve a significant increase in accuracy compared to existing models based on all three datasets, and it consistently outperforms all tested baselines, including state-of-the-art techniques.
An Information Maximization Multi-task Clustering Method for egocentric temporal segmentation

With the widespread application of vision-based wearable devices, temporal segmentation helps people search for and localize all occurrences quickly in egocentric videos. In the same scenario, the activities are similar to each other, e.g., people staying at home typically cook, clean and watch TV. These relations among videos of different individuals are regarded as auxiliary information to improve task performance. Inspired by this, we propose an Information Maximization Multi-task Clustering (IMMC) algorithm for egocentric temporal segmentation. The algorithm mainly includes two parts: (1) within-task clustering: clustering on each task based on an information maximization approach, and (2) cross-task information transferring: a novel strategy is presented to transfer correlation information between tasks, which balances the correlation among clusters in different tasks to improve the performance of the individual task. A draw-merge method is designed to address the optimization problem. Experiments are performed on three publicly available first-person vision data sets and a new data set we construct (Outdoor data set). The results show that IMMC consistently outperforms the state-of-the-art clustering methods in multiple evaluation metrics. Moreover, it achieves relatively good performance on runtime cost and convergence.
Velocity Measurement of Blast Furnace Molten Iron Based on Local Multi-Feature Correction Using Multi-Stage Filtered High-Speed Camera

Accurately measuring the velocity of molten iron at the taphole of a blast furnace is of great significance for safe tapping and reflecting the operating state of the blast furnace. The presence of dust-induced brightness changes and weakly textured areas on the surface of molten iron seriously affects the accuracy of the surface velocity field reconstructed by traditional methods. This paper presents a local multi-feature correction using multi-stage filtered high-speed camera to achieve high-precision measurement of the surface velocity field of molten iron. First, two sets of local molten iron features were extracted according to a theoretical analysis of the influence of dust-induced brightness changes and the molten iron texture on the accuracy of combined local-global approach with total variation regularization (CLG-TV) and digital image correlation (DIC). Then, a mixed semifuzzy approximation network based on the characteristics of local molten iron features is proposed to estimate measurement errors under the influence of brightness changes and weak textures. Finally, an ingenious decision maker is designed to determine a more accurate displacement field of the molten iron surface. Additionally, a designed multi-stage filtered high-speed camera is installed beside the taphole to provide good optical images of molten iron surface. Industrial experiments and applications illustrate that the proposed method can stably and accurately measure the molten iron velocity at the taphole and provide reliable guidance for tapping operations.
A Virtual Reality Couch Configurator Leveraging Passive Haptic Feedback

When configuring furniture during sales consultancy in a furniture store, customers are usually confronted with abstract 2D drawings or simplistic renderings of the discussed configuration on a display. We present a novel application based on virtual reality (VR) to support furniture store consultations. Our system allows customers to elaborate different configurations of a couch in dialogue with a sales expert and lets customers experience them through immersive VR in a variety of virtual environments. While the sales-expert can modify the couch layout and fabric, the customer can stay immersed and experience a realistic tactile feeling of the configured couch through passive haptic feedback provided by a sample piece the customer can sit on. A preliminary field study in a furniture store showed that the system is immersive, conveying realistic impressions of the couch configurations. Customers perceived the VR configurator as useful since it would make their purchase decisions easier.
Symbols in Engineering Drawings (SiED): An Imbalanced Dataset Benchmarked by Convolutional Neural Networks

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed.
Tampering with Digital Evidence is Hard: The Case of Main Memory Images

Tampered digital evidence may jeopardize its correct interpretation. To assess the risks in a court of law, it is helpful to quantify the necessary effort to perform a convincing manipulation of digital evidence. Based on a sequence of controlled experiments with graduate students and digital forensics professionals, we study the effort to manipulate copies of main memory taken during a digital investigation. Confirming previous results on hard disc image tampering, manipulating main memory dumps can be considered hard in the sense that most forgeries were successfully detected. However, while the effort to detect a manipulation is generally bounded by the tampering effort, some forgeries fooled the analysts and caused analysis effort that was higher than the manipulation effort. The detection effort by graduate students, however, was generally higher than that of professionals. We study different manipulation and detection approaches and their success. Overall, tampering with main memory dumps appears to be harder than tampering with hard disc images but the probability to fool an analyst is higher too.
Accelerating Adult Readers with Typeface: A Study of Individual Preferences and Effectiveness

Information overload is the challenge of the modern era and text the medium. Every adult reader would benefit from faster reading, provided they could retain comprehension. The present work explores the reading speed gains possible solely by manipulating typeface. We consider that optimal typeface might be a matter of an individual's preferred font, or that some fonts might be better for all users. Indeed, eight in ten of our participants believed their favorite font would be their best. Instead, our findings showed that the preferred font was seldom best, and one font did not fit all. Adult readers in our study read better with varying fonts. An average 117 word per minute difference between worst and best typeface, or around 10 additional pages an hour, means font choice is of real-world significance. Our discussion focuses on the challenges of rapidly identifying an individual's optimal font, and the exciting individuation technologies such an advance allows.
Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine

Abstract One of the challenging tasks in the medicinal field is genomic disorder investigation and its classification from the microarray dataset. The microarray dataset reorganization and its classification is more complex and expensive in the biomedical research area due to the larger number of features in the microarray dataset. In this paper, we construct a hybrid feature selection method such as t test, Fisher ration, and Bayesian logistic regression to select genes and that reduce the time cost. Based on the features, the top‐ranked features are selected via the best hybrid rank method. Thereafter, the features are extracted using the modified firefly optimization‐based discriminant independent component analysis (MF‐DICA). Especially, the modified firefly optimization algorithm is capable of improving the search efficiency of DICA. From the high dimensional microarray dataset, MF‐DICA is used to obtain the best features within the entire search space. The kernel extreme learning machine classifies the gene features depending upon the most relevant class. Experimentally, six datasets namely Leukemia dataset, Diffuse Larger B‐cell Lymphomas, Lung cancer, Breast cancer, Prostate tumor, and Colon dataset are chosen to evaluate the performance of proposed approaches. Finally, the experimental data demonstrate that the proposed method is well suitable to classify the microarray data.
T0RTT: Non-Interactive Immediate Forward-Secret Single-Pass Circuit Construction

Abstract Maintaining privacy on the Internet with the presence of powerful adversaries such as nation-state attackers is a challenging topic, and the Tor project is currently the most important tool to protect against this threat. The circuit construction protocol (CCP) negotiates cryptographic keys for Tor circuits, which overlay TCP/IP by routing Tor cells over n onion routers. The current circuit construction protocol provides strong security guarantees such as forward secrecy by exchanging 𝒪 ( n 2 ) messages. For several years it has been an open question if the same strong security guarantees could be achieved with less message overhead, which is desirable because of the inherent latency in overlay networks. Several publications described CCPs which require only 𝒪 ( n ) message exchanges, but significantly reduce the security of the resulting Tor circuit. It was even conjectured that it is impossible to achieve both message complexity 𝒪 ( n ) and forward secrecy immediately after circuit construction (so-called immediate forward secrecy). Inspired by the latest advancements in zero round-trip time key exchange (0-RTT), we present a new CCP protocol Tor 0-RTT (T0RTT) . Using modern cryptographic primitives such as puncturable encryption allow to achieve immediate forward secrecy using only 𝒪 ( n ) messages. We implemented these new primitives to give a first indication of possible problems and how to overcome them in order to build practical CCPs with 𝒪 ( n ) messages and immediate forward secrecy in the future.
QuRate

Smartphones have recently become a popular platform for deploying the computation-intensive virtual reality (VR) applications, such as immersive video streaming (a.k.a., 360-degree video streaming). One specific challenge involving the smartphone-based head mounted display (HMD) is to reduce the potentially huge power consumption caused by the immersive video. To address this challenge, we first conduct an empirical power measurement study on a typical smartphone immersive streaming system, which identifies the major power consumption sources. Then, we develop QuRate, a quality-aware and user-centric frame rate adaptation mechanism to tackle the power consumption issue in immersive video streaming. QuRate optimizes the immersive video power consumption by modeling the correlation between the perceivable video quality and the user behavior. Specifically, QuRate builds on top of the user's reduced level of concentration on the video frames during view switching and dynamically adjusts the frame rate without impacting the perceivable video quality. We evaluate QuRate with a comprehensive set of experiments involving 5 smartphones, 21 users, and 6 immersive videos using empirical user head movement traces. Our experimental results demonstrate that QuRate is capable of extending the smartphone battery life by up to 1.24X while maintaining the perceivable video quality during immersive video streaming. Also, we conduct an Institutional Review Board (IRB)-approved subjective user study to further validate the minimum video quality impact caused by QuRate.
FoolChecker: A platform to evaluate the robustness of images against adversarial attacks

Deep neural network (DNN) is inherently vulnerable to well-designed input samples called adversarial examples, which can easily alter the output of the DNN by adding slight perturbations to the input. The recent study proved that adversarial vulnerability is caused by non-robust features and is not inherently tied to DNN. The paper presents a platform called FoolChecker to evaluate the image robustness against adversarial attacks from the perspective of image itself rather than DNN models. We define the minimum perceptual distance between the original examples and the adversarial ones to quantify the robustness against adversarial attacks. Firstly, differential evolution is applied to generate candidate perturbation units with high perturbation priority. And then, the greedy algorithm tries to add the pixel with the current highest perturbation priority into perturbation units until the DNN model is fooled. Finally, the perceptual distance of perturbation units is calculated as a index to evaluate the robustness of images against adversarial attacks. Experimental results show that the FoolChecker can give proper evaluation of the robustness of images against adversarial attacks with acceptable time.
SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data

Speech-driven querying is becoming popular in new device environments such as smartphones, tablets, and even conversational assistants. However, such querying is largely restricted to natural language. Typed SQL remains the gold standard for sophisticated structured querying although it is painful in many environments, which restricts when and how users consume their data. In this work, we propose to bridge this gap by designing a speech-driven querying system and interface for structured data we call SpeakQL. We support a practically useful subset of regular SQL and allow users to query in any domain with novel touch/speech based human-in-the-loop correction mechanisms. Automatic speech recognition (ASR) introduces myriad forms of errors in transcriptions, presenting us with a technical challenge. We exploit our observations of SQL's properties, its grammar, and the queried database to build a modular architecture. We present the first dataset of spoken SQL queries and a generic approach to generate them for any arbitrary schema. Our experiments show that SpeakQL can automatically correct a large fraction of errors in ASR transcriptions. User studies show that SpeakQL can help users specify SQL queries significantly faster with a speedup of average 2.7x and up to 6.7x compared to typing on a tablet device. SpeakQL also reduces the user effort in specifying queries by a factor of average 10x and up to 60x compared to raw typing effort.
Onboarding: How Newcomers Integrate into an Agile Project Team

Although a stable team is deemed optimal for agile project success, new team members need to join ongoing agile projects. Newcomers must rapidly assimilate into the organisational and project environment while learning how to contribute effectively to the project and integrate into the team without seriously interrupting project progress. This paper addresses how newcomers integrate into an established agile project team and the challenges newcomers and the team face during this process. This paper is a single case study of a co-located agile project team in a large IT department who regularly onboard inexperienced newcomers. We found a mixture of traditional onboarding practices and specific agile practices contribute to the onboarding process. Onboarding challenges include empowerment and mindset change, accommodating part-timers, conveying agile principles, and adjusting to changes in team composition.