nils-herrmann/clean_scirepeval huggingface dataset:
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks

We propose a parallel-data-free voice-conversion (VC) method that can learn a mapping from source to target speech without relying on parallel data. The proposed method is general purpose, high quality, and parallel-data free and works without any extra data, modules, or alignment procedure. It also avoids over-smoothing, which occurs in many conventional statistical model-based VC methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial network (CycleGAN) with gated convolutional neural networks (CNNs) and an identity-mapping loss. A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses. This makes it possible to find an optimal pseudo pair from unpaired data. Furthermore, the adversarial loss contributes to reducing over-smoothing of the converted feature sequence. We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss. This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information. We evaluated our method on a parallel-data-free VC task. An objective evaluation showed that the converted feature sequence was near natural in terms of global variance and modulation spectra. A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture modelbased method under advantageous conditions with parallel and twice the amount of data.
A Survey of Recent Trends in One Class Classification

The One Class Classification (OCC) problem is different from the conventional binary/multi-class classification problem in the sense that in OCC, the negative class is either not present or not properly sampled. The problem of classifying positive (or target) cases in the absence of appropriately-characterized negative cases (or outliers) has gained increasing attention in recent years. Researchers have addressed the task of OCC by using different methodologies in a variety of application domains. In this paper we formulate a taxonomy with three main categories based on the way OCC has been envisaged, implemented and applied by various researchers in different application domains. We also present a survey of current state-of-the-art OCC algorithms, their importance, applications and limitations.
MQTT based vehicle accident detection and alert system

Now-a-days as we open the newspaper, we find atleast one news of a road accident. With vehicles becoming increasingly affordable, there has been a surge in the number of vehicles on roads on an average all over the world. Accidents bring devastation upon victims, causing loss of precious time and money. It has been established, after extensive research, that a majority of accidents become fatalities because of lack of communication to the concerned medical authorities and the consequent lack of immediate medical support. This application helps sense the possible occurence of an accident on the road, with the help of sensors attached to the vehicle. This occurence will be immediately communicated to the concerned people so that further action can be taken without any further ado.
Credibility Assessment in the News : Do we need to read ?

While news media biases and propaganda are a persistent problem for interpreting the true state of world affairs, increasing reliance on the internet as a primary news source has enabled the formation of hyper-partisan echo chambers and an industry where outlets benefit from purveying “fake news”. The presence of intentionally adversarial news sources challenges linguistic modeling of news article text. While modeling text content of articles is sufficient to identify bias, it is not capable of determining credibility. A structural model based on web links outperforms text models for fake news detection. Analysis of text based methods for bias detection reveals the existence of liberal words and conservative words, but there is no analogue for fake news words versus real news words.
Automated Analysis and Prediction of Job Interview Performance

We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews. The proposed framework is trained by analyzing the videos of 138 interview sessions with 69 internship-seeking undergraduates at the Massachusetts Institute of Technology (MIT). Our automated analysis includes facial expressions (e.g., smiles, head gestures, facial tracking points), language (e.g., word counts, topic modeling), and prosodic information (e.g., pitch, intonation, and pauses) of the interviewees. The ground truth labels are derived by taking a weighted average over the ratings of nine independent judges. Our framework can automatically predict the ratings for interview traits such as excitement, friendliness, and engagement with correlation coefficients of 0.70 or higher, and can quantify the relative importance of prosody, language, and facial expressions. By analyzing the relative feature weights learned by the regression models, our framework recommends to speak more fluently, use fewer filler words, speak as “we” (versus “I”), use more unique words, and smile more. We also find that the students who were rated highly while answering the first interview question were also rated highly overall (i.e., first impression matters). Finally, our MIT Interview dataset is available to other researchers to further validate and expand our findings.
Automatic detection of arteriovenous nicking in retinal images

Morphological changes of retinal vessels such as arteriovenous (AV) nicking are signs of many systemic diseases. In this paper, an automatic method for AV-nicking detection is proposed. The proposed method includes crossover point detection and AV-nicking identification. Vessel segmentation, vessel thinning, and feature point recognition are performed to detect crossover point. A method of vessel diameter measurement is proposed with processing of removing voids, hidden vessels and micro-vessels in segmentation. The AV-nicking is detected based on the features of vessel diameter measurement. The proposed algorithms have been tested using clinical images. The results show that nicking points in retinal images can be detected successfully in most cases.
Modeling POI Transition Network of Human Mobility

Point-of-interest (POI) denotes the category of a location people visit, reflecting the daily life and personal interests. Through a massive dataset of check-in records, this paper discloses that the POI visiting pattern of human spatial mobility falling into both Zipf's law and Heaps' law. It is found that people always have three potential choices when selecting a particular POI as their next travel destination, namely, exploring a new POI, returning to a visited one, or staying at the current one. We calculate the probabilities of these scenarios and propose a statistical model based on random walk to represent such phenomena. Simulation results show that our model can well reproduce the heterogeneous Zipf's law and Heaps' law of human mobility.
Surface plasmon enhanced silicon solar cells

Thin-film solar cells have the potential to significantly decrease the cost of photovoltaics. Light trapping is particularly critical in such thin-film crystalline silicon solar cells in order to increase light absorption and hence cell efficiency. In this article we investigate the suitability of localized surface plasmons on silver nanoparticles for enhancing the absorbance of silicon solar cells. We find that surface plasmons can increase the spectral response of thin-film cells over almost the entire solar spectrum. At wavelengths close to the band gap of Si we observe a significant enhancement of the absorption for both thin-film and wafer-based structures. We report a sevenfold enhancement for wafer-based cells at =1200 nm and up to 16-fold enhancement at =1050 nm for 1.25 m thin silicon-on-insulator SOI cells, and compare the results with a theoretical dipole-waveguide model. We also report a close to 12-fold enhancement in the electroluminescence from ultrathin SOI light-emitting diodes and investigate the effect of varying the particle size on that enhancement.
Overlapping Community Structure in Co-authorship Networks: A Case Study

Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network rep-resent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological properties of these two networks shows that they share similar topological properties. These results are very interesting. Indeed, the network of communities seems to be a good representative of the original co-authorship network. With its smaller size, it may be more practical in order to realize various analyses that cannot be performed easily in large-scale real-world networks.
Catadioptric Stereo Using Planar Mirrors

By using mirror reflections of a scene, stereo images can be captured with a single camera (catadioptric stereo). In addition to simplifying data acquisition single camera stereo provides both geometric and radiometric advantages over traditional two camera stereo. In this paper, we discuss the geometry and calibration of catadioptric stereo with two planar mirrors. In particular, we will show that the relative orientation of a catadioptric stereo rig is restricted to the class of planar motions thus reducing the number of external calibration parameters from 6 to 5. Next we derive the epipolar geometry for catadioptric stereo and show that it has 6 degrees of freedom rather than 7 for traditional stereo. Furthermore, we show how focal length can be recovered from a single catadioptric image solely from a set of stereo correspondences. To test the accuracy of the calibration we present a comparison to Tsai camera calibration and we measure the quality of Euclidean reconstruction. In addition, we will describe a real-time system which demonstrates the viability of stereo with mirrors as an alternative to traditional two camera stereo.
Smart City and Smart Tourism : A Case of Dubai

Over the past decade, the advent of new technology has brought about the emergence of smart cities aiming to provide their stakeholders with technology-based solutions that are effective and efficient. Insofar as the objective of smart cities is to improve outcomes that are connected to people, systems and processes of businesses, government and other publicand private-sector entities, its main goal is to improve the quality of life of all residents. Accordingly, smart tourism has emerged over the past few years as a subset of the smart city concept, aiming to provide tourists with solutions that address specific travel related needs. Dubai is an emerging tourism destination that has implemented smart city and smart tourism platforms to engage various stakeholders. The objective of this study is to identify best practices related to Dubai’s smart city and smart tourism. In so doing, Dubai’s mission and vision along with key dimensions and pillars are identified in relation to the advancements in the literature while highlighting key resources and challenges. A Smart Tourism Dynamic Responsive System (STDRS) framework is proposed while suggesting how Dubai may able to enhance users’ involvement and their overall experience.
Privacy-Preserving Detection of Sensitive Data Exposure

Statistics from security firms, research institutions and government organizations show that the number of data-leak instances have grown rapidly in recent years. Among various data-leak cases, human mistakes are one of the main causes of data loss. There exist solutions detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. A common approach is to screen content in storage and transmission for exposed sensitive information. Such an approach usually requires the detection operation to be conducted in secrecy. However, this secrecy requirement is challenging to satisfy in practice, as detection servers may be compromised or outsourced. In this paper, we present a privacy-preserving data-leak detection (DLD) solution to solve the issue where a special set of sensitive data digests is used in detection. The advantage of our method is that it enables the data owner to safely delegate the detection operation to a semihonest provider without revealing the sensitive data to the provider. We describe how Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. The evaluation results show that our method can support accurate detection with very small number of false alarms under various data-leak scenarios.
Colwell's Castle Defence: A Custom Game Using Dynamic Difficulty Adjustment to Increase Player Enjoyment

Dynamic Difficulty Adjustment (DDA) is a mechanism used in video games that automatically tailors the individual gaming experience to match an appropriate difficulty setting. This is generally achieved by removing pre-defined difficulty tiers such as Easy, Medium and Hard; and instead concentrates on balancing the gameplay to match the challenge to the individual’s abilities. The work presented in this paper examines the implementation of DDA in a custom survival game developed by the author, namely Colwell’s Castle Defence. The premise of this arcade-style game is to defend a castle from hordes of oncoming enemies. The AI system that we developed adjusts the enemy spawn rate based on the current performance of the player. Specifically, we read the Player Health and Gate Health at the end of each level and then assign the player with an appropriate difficulty tier for the proceeding level. We tested the impact of our technique on thirty human players and concluded, based on questionnaire feedback, that enabling the technique led to more enjoyable gameplay.
Towards Automatic & Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective

Machine learning (ML) is the fastest growing field in computer science and healthcare, providing future benefits in improved medical diagnoses, disease analyses and prevention. In this paper, we introduce an application of interactive machine learning (iML) in a telemedicine system, to enable automatic and personalised interventions for lifestyle promotion. We first present the high level architecture of the system and the components forming the overall architecture. We then illustrate the interactive machine learning process design. Prediction models are expected to be trained through the participants’ profiles, activity performance, and feedback from the caregiver. Finally, we show some preliminary results during the system implementation and discuss future directions. We envisage the proposed system to be digitally implemented, and behaviourally designed to promote healthy lifestyle and activities, and hence prevent users from the risk of chronic diseases.
Intrusion detection in Bluetooth enabled mobile phones

Bluetooth plays a major role in expanding global spread of wireless technology. This predominantly happens through Bluetooth enabled mobile phones, which cover almost 60% of the Bluetooth market. Although Bluetooth mobile phones are equipped with built-in security modes and policies, intruders compromise mobile phones through existing security vulnerabilities and limitations. Information stored in mobile phones, whether it is personal or corporate, is significant to mobile phone users. Hence, the need to protect information, as well as alert mobile phone users of their incoming connections, is vital. An additional security mechanism was therefore conceptualized, at the mobile phone's user level, which is essential in improving the security. Bluetooth Logging Agent (BLA) is a mechanism that has been developed for this purpose. It alleviates the current security issues by making the users aware of their incoming Bluetooth connections and gives them an option to either accept or reject these connections. Besides this, the intrusion detection and verification module uses databases and rules to authenticate and verify all connections. BLA when compared to the existing security solutions is novel and unique in that it is equipped with a Bluetooth message logging module. This logging module reduces the security risks by monitoring the Bluetooth communication between the mobile phone and the remote device.
Towards More Human Like Reinforcement Learning

Making machines more intelligent can potentially make human life easier. A lot of research has gone into the field of artificial intelligence (AI) since the creation of first computers. However, today’s systems still lag behind humans’ general ability to think and learn. Reinforcement Learning (RL) is a framework where software agents learn by interaction with an environment. We investigate possibilities to use observations about human intelligence to make RL agents smarter. In particular, we tried several methods: 1) To use “Tagger” an unsupervised deep learning framework for perceptual grouping, to learn more usable abstract relationships between objects. 2) Make one RL algorithm (A3C) more data efficient to learn faster. 3) To conduct these experiments, we built a web based RL dashboard based on visualization tool visdom. Finally, we provide some concrete challenges to work on in the future.
Regression Analysis of Longitudinal Data in the Presence of Informative Observation and Censoring Times

Longitudinal data frequently occur in many studies, such as longitudinal follow-up studies. To develop statistical methods and theory for the analysis of these data, independent or noninformative observation and censoring times are typically assumed, which naturally leads to inference procedures conditional on observation and censoring times. But in many situations this may not be true or realistic; that is, longitudinal responses may be correlated with observation times as well as censoring times. This article considers the analysis of longitudinal data where these correlations may exist and proposes a joint modeling approach that uses some latent variables to characterize the correlations. For inference about regression parameters, estimating equation approaches are developed and both large-sample and finalsample properties of the proposed estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The methodology is applied to a bladder cancer study that motivated this investigation.
Automatic text summarization using fuzzy inference

Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods.
A universally calibrated microplate ferric reducing antioxidant power (FRAP) assay for foods and applications to Manuka honey.

The ferric reducing antioxidant power (FRAP) assay was recently adapted to a microplate format. However, microplate-based FRAP (mFRAP) assays are affected by sample volume and composition. This work describes a calibration process for mFRAP assays which yields data free of volume effects. From the results, the molar absorptivity (ε) for the mFRAP assay was 141,698 M(-1) cm(-1) for gallic acid, 49,328 M(-1) cm(-1) for ascorbic acid, and 21,606 M(-1) cm(-1) for ammonium ferrous sulphate. The significance of ε (M(-1) cm(-1)) is discussed in relation to mFRAP assay sensitivity, minimum detectable concentration, and the dimensionless FRAP-value. Gallic acid showed 6.6 mol of Fe(2+) equivalents compared to 2.3 mol of Fe(+2) equivalents for ascorbic acid. Application of the mFRAP assay to Manuka honey samples (rated 5+, 10+, 15+, and 18+ Unique Manuka Factor; UMF) showed that FRAP values (0.54-0.76 mmol Fe(2+) per 100g honey) were strongly correlated with UMF ratings (R(2)=0.977) and total phenols content (R(2) = 0.982)whilst the UMF rating was correlated with the total phenols (R(2) = 0.999). In conclusion, mFRAP assay results were successfully standardised to yield data corresponding to 1-cm spectrophotometer which is useful for quality assurance purposes. The antioxidant capacity of Manuka honey was found to be directly related to the UMF rating.
A novel hybrid CMOS-memristor logic circuit using Memristor Ratioed Logic

Memristors can be optimally used to implement logic circuits. In this paper, a logic circuit based on Memristor Ratioed Logic (MRL) is proposed. Specifically, a hybrid CMOS-memristive logic family by a suitable combination of 4 memristor and a complementary inverter CMOS structure is presented. The proposed structure by having outputs of AND, OR and XOR gates of inputs at the same time, reducing the area and connections and fewer power consumption can be appropriate for implementation of more complex circuits. Circuit design of a single-bit Full Adder is considered as a case study. The Full Adder proposed is implemented using 10 memristors and 4 transistors comparing to 18 memristors and 8 transistors in the other related work.
Dataset Augmentation for Pose and Lighting Invariant Face Recognition

The performance of modern face recognition systems is a function of the dataset on which they are trained. Most datasets are largely biased toward “near-frontal” views with benign lighting conditions, negatively effecting recognition performance on images that do not meet these criteria. The proposed approach demonstrates how a baseline training set can be augmented to increase pose and lighting variability using semisynthetic images with simulated pose and lighting conditions. The semi-synthetic images are generated using a fast and robust 3d shape estimation and rendering pipeline which includes the full head and background. Various methods of incorporating the semi-synthetic renderings into the training procedure of a state of the art deep neural network-based recognition system without modifying the structure of the network itself are investigated. Quantitative results are presented on the challenging IJB-A identification dataset using a state of the art recognition pipeline as a baseline.