Miracle-dz/UniFAR-FTUs huggingface dataset:
A punishment voting algorithm based on super categories construction for acoustic scene classification

In acoustic scene classification researches, audio segment is usually split into multiple samples. Majority voting is then utilized to ensemble the results of the samples. In this paper, we propose a punishment voting algorithm based on the super categories construction method for acoustic scene classification. Specifically, we propose a DenseNet-like model as the base classifier. The base classifier is trained by the CQT spectrograms generated from the raw audio segments. Taking advantage of the results of the base classifier, we propose a super categories construction method using the spectral clustering. Super classifiers corresponding to the constructed super categories are further trained. Finally, the super classifiers are utilized to enhance the majority voting of the base classifier by punishment voting. Experiments show that the punishment voting obviously improves the performances on both the DCASE2017 Development dataset and the LITIS Rouen dataset.
DialPlate: Enhancing the Detection of Smooth Pursuits Eye Movements Using Linear Regression

We introduce and evaluate a novel approach for detecting smooth pursuit eye movements that increases the number of distinguishable targets and is more robust against false positives. Being natural and calibration-free, Pursuits has been gaining popularity in the past years. At the same time, current implementations show poor performance when more than eight on-screen targets are being used, thus limiting its applicability. Our approach (1) leverages the slope of a regression line, and (2) introduces a minimum signal duration that improves both the new and the traditional detection method. After introducing the approach as well as the implementation, we compare it to the traditional correlation-based Pursuits detection method. We tested the approach up to 24 targets and show that, if accepting a similar error rate, nearly twice as many targets can be distinguished compared to state of the art. For fewer targets, accuracy increases significantly. We believe our approach will enable more robust pursuit-based user interfaces, thus making it valuable for both researchers and practitioners.
Generating Recommendations by Graph Traversal in Social Rating Networks

Collaborative filtering recommender systems make automatic predictions about users' interests by utilizing information collected from similarly minded users in order to recommend new items. However in most practical settings the ratings matrix is extremely sparse and thus the step of calculating similarities between users often fails. We propose a method for generating recommendations in such problematic cases by expanding and traversing the users' similarity graph so as to make new link predictions between users that are not directly connected. We evaluate our proposal on the Epin-ions social rating network, and show that the infusion of this new information in the similarity graph is comparable to state of the art graph embedding techniques but with lower computational cost and directly comparable coverage.
Implicit User Modeling in Group Chat

In recent years, enterprise group chat collaboration tools such as Slack, IBM's Watson Workspace and Microsoft Teams, have presented unprecedented growth. With all the potential benefits of these tools " productivity increase and improved group communication " come significant challenges. Specifically, users find it hard to focus their attention on content that is relevant to them due to the load of conversational content. This load can be handled by personalized content presentation and summarization mitigated by user profiling. We present an unsupervised approach for implicitly modeling group chat users through a combination of a probabilistic topic model and social analysis. We evaluate our approach by testing it on a task of conversation participation prediction, serving as a proxy for anticipating user interests, and show that by utilizing our approach, a system successfully predicts users participation in conversations. We further analyze the contribution of the various user model components and show them to be significant.
Mining Free-Text Medical Notes for Suicide Risk Assessment

Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.
The cranial endocast of the Upper Devonian dipnoan ‘Chirodipterus’ australis

One of the first endocasts of a dipnoan (lungfish) to be realised was that of the Upper Devonian taxon Chirodipterus australis. This early interpretation was based on observations of the shape of the cranial cavity alone and was not based on a natural cast or ‘steinkern’ nor from serial sectioning. The validity of this reconstruction is therefore questionable and continued reference to and use of this interpretation in analyses of sarcopterygian cranial evolution runs the risk of propagation of error. Here we present a new detailed anatomical description of the endocast of ‘Chirodipterus’ australis from the Upper Devonian Gogo Formation of Western Australia, known for exceptional 3D preservation which enables fine-scale scrutiny of endocranial anatomy. We show that it exhibits a suite of characters more typical of Lower and Middle Devonian dipnoan taxa. Notably, the small utricular recess is unexpected for a taxon of this age, whereas the ventral expansion of the telencephalon is more typical of more derived taxa. The presence of such ’primitive’ characters in ‘C. ’ australis supports its relatively basal position as demonstrated in the most recent phylogenies of Devonian Dipnoi.
Approximate k-Nearest Neighbor Search Based on the Earth Mover's Distance for Efficient Content-based Information Retrieval

The Earth Mover's Distance (EMD) is one of the most-widely used distance functions to measure the similarity between two multimedia objects. While providing good search results, the EMD is too much time-consuming to be used in large multimedia databases. To solve the problem, we propose an approximate k-nearest neighbor (k-NN) search method based on the EMD. First, the proposed method builds an index using the M-tree, a distance-based multi-dimensional index structure, to reduce the disk access overhead. When building the index, we reduce the number of features in the multimedia objects through dimensionality-reduction. When performing the k-NN search on the M-tree, we find a small set of candidates from the disk using the index and then perform the post-processing on them. Second, the proposed method uses the approximate EMD for index retrieval and post-processing to reduce the computational overhead of the EMD. To compensate the errors due to the approximation, the method provides a way of accuracy improvement of the approximate EMD. We performed extensive experiments to show the efficiency of the proposed method.
FINN-L: Library Extensions and Design Trade-Off Analysis for Variable Precision LSTM Networks on FPGAs

It is well known that many types of artificial neural networks, including recurrent networks, can achieve a high classification accuracy even with low-precision weights and activations. The reduction in precision generally yields much more efficient hardware implementations in regards to hardware cost, memory requirements, energy, and achievable throughput. In this paper, we present the first systematic exploration of this design space as a function of precision for Bidirectional Long Short-Term Memory (BiLSTM) neural network. Specifically, we include an in-depth investigation of precision vs. accuracy using a fully hardware-aware training flow, where during training quantization of all aspects of the network including weights, input, output and in-memory cell activations are taken into consideration. In addition, hardware resource cost, power consumption and throughput scalability are explored as a function of precision for FPGA-based implementations of BiLSTM, and multiple approaches of parallelizing the hardware. We provide the first open source HLS library extension of FINN for parameterizable hardware architectures of LSTM layers on FPGAs which offers full precision flexibility and allows for parameterizable performance scaling offering different levels of parallelism within the architecture. Based on this library, we present an FPGA-based accelerator for BiLSTM neural network designed for optical character recognition, along with numerous other experimental proof points for a Zynq UltraScale+ XCZU7EV MPSoC within the given design space.
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection

Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency. Smart contracts are programs running on top of the blockchain consensus protocol to enable people make agreements while minimizing trusts. Millions of smart contracts have been deployed in various decentralized applications. The security vulnerabilities within those smart contracts pose significant threats to their applications. Indeed, many critical security vulnerabilities within smart contracts on Ethereum platform have caused huge financial losses to their users. In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities. ContractFuzzer generates fuzzing inputs based on the ABI specifications of smart contracts, defines test oracles to detect security vulnerabilities, instruments the EVM to log smart contracts runtime behaviors, and analyzes these logs to report security vulnerabilities. Our fuzzing of 6991 smart contracts has flagged more than 459 vulnerabilities with high precision. In particular, our fuzzing tool successfully detects the vulnerability of the DAO contract that leads to $60 million loss and the vulnerabilities of Parity Wallet that have led to the loss of $30 million and the freezing of $150 million worth of Ether.
Neuro-Symbolic Program Corrector for Introductory Programming Assignments

Automatic correction of programs is a challenging problem with numerous real world applications in security, verification, and education. One application that is becoming increasingly important is the correction of student submissions in online courses for providing feedback. Most existing program repair techniques analyze Abstract Syntax Trees (ASTs) of programs, which are unfortunately unavailable for programs with syntax errors. In this paper, we propose a novel Neuro-symbolic approach that combines neural networks with constraint-based reasoning. Specifically, our method first uses a Recurrent Neural Network (RNN) to perform syntax repairs for the buggy programs; subsequently, the resulting syntactically-fixed programs are repaired using constraint-based techniques to ensure functional correctness. The RNNs are trained using a corpus of syntactically correct submissions for a given programming assignment, and are then queried to fix syntax errors in an incorrect programming submission by replacing or inserting the predicted tokens at the error location. We evaluate our technique on a dataset comprising of over 14,500 student submissions with syntax errors. Our method is able to repair syntax errors in 60% (8689) of submissions, and finds functionally correct repairs for 23.8% (3455) submissions.
Relation of Enteric α-Synuclein to Gastrointestinal Dysfunction in Patients With Parkinson’s Disease and in Neurologically Intact Subjects

Background/Aims α-Synucleinopathy in the brain is the neuropathological hallmark of Parkinson’s disease (PD). However, the functional impact of α-synucleinopathy in the enteric nervous system remains unknown. We aim to evaluate the association between gastrointestinal (GI) dysfunction and α-synuclein (αSYN) pathology in the stomach and colon of PD patients and controls, as well as to investigate the association between the αSYN pathology in GI tract and future PD risk. Methods A total of 35 PD patients and 52 neurologically intact subjects were enrolled in this study. Endoscopic biopsies were performed, and then immunohistochemical staining for αSYN was performed. All subjects completed the validated Rome III questionnaire for the assessment of GI symptoms. The association between GI symptoms and the αSYN pathology in GI mucosa was evaluated. Incident PD cases were assessed during a median follow-up of 46 months. Results The proportion of self-reported constipation and functional constipation through the Rome III questionnaire was significantly higher in PD patients than in controls (P < 0.001 and P = 0.015). However, no significant association was found between the αSYN pathology in the stomach and colon mucosa and constipation, as well as other GI symptoms including dyspepsia symptoms and abdominal discomfort or pain, regardless of the presence or absence of clinical PD (P > 0.05). No incident PD cases were diagnosed during study period. Conclusions Our present study suggests that the deposition of αSYN in the mucosal enteric nervous system may not be reflected by functional impairment of the affected segment of the gut.
Situation Calculus for Synthesis of Manufacturing Controllers

Manufacturing is transitioning from a mass production model to a manufacturing as a service model in which manufacturing facilities 'bid' to produce products. To decide whether to bid for a complex, previously unseen product, a manufacturing facility must be able to synthesize, 'on the fly', a process plan controller that delegates abstract manufacturing tasks in the supplied process recipe to the appropriate manufacturing resources, e.g., CNC machines, robots etc. Previous work in applying AI behaviour composition to synthesize process plan controllers has considered only finite state ad-hoc representations. Here, we study the problem in the relational setting of the Situation Calculus. By taking advantage of recent work on abstraction in the Situation Calculus, process recipes and available resources are represented by ConGolog programs over, respectively, an abstract and a concrete action theory. This allows us to capture the problem in a formal, general framework, and show decidability for the case of bounded action theories. We also provide techniques for actually synthesizing the controller.
A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion

Recommending venues plays a critical rule in satisfying users' needs on location-based social networks. Recent studies have explored the idea of adopting collaborative ranking (CR) for recommendation, combining the idea of learning to rank and collaborative filtering. However, CR suffers from the sparsity problem, mainly because it associates similar users based on exact matching of the venues in their check-in history. Even though research in collaborative filtering has shown that considering auxiliary information such as geographical influence, helps the model to alleviate the sparsity problem, the same direction still needs to be explored in CR. In this work, we present a CR framework that focuses on the top of the ranked list while integrating an arbitrary number of similarity functions between venues as it learns the model's parameters. We further introduce three example similarity measures based on venues' contents and locations. Incorporating cross-venue similarity measures into the model enhances the latent associations between users as similar venues are also taken into account while associating users with each other. Our experiments on the TREC Contextual Suggestion dataset show that our proposed CR model beats other state-of-the-art venue suggestion methods.
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r. t. different styles.
Collaborative Filtering with Behavioral Models

Collaborative filtering (CF) has made it possible to build personalized recommendation models leveraging the collective data of large user groups, albeit with prescribed models that cannot easily leverage the existence of known behavioral models in particular settings. In this paper, we facilitate the combination of CF with existing behavioral models by introducing Bayesian Behavioral Collaborative Filtering (BBCF). BBCF works by embedding arbitrary (black-box) probabilistic models of human behavior in a latent variable Bayesian framework capable of collectively leveraging behavioral models trained on all users for personalized recommendation. There are three key advantages of BBCF compared to traditional CF and non-CF methods: (1) BBCF can leverage highly specialized behavioral models for specific CF use cases that may outperform existing generic models used in standard CF, (2) the behavioral models used in BBCF may offer enhanced intepretability and explainability compared to generic CF methods, and (3) compared to non-CF methods that would train a behavioral model per specific user and thus may suffer when individual user data is limited, BBCF leverages the data of all users thus enabling strong performance across the data availability spectrum including the near cold-start case. Experimentally, we compare BBCF to individual and global behavioral models as well as CF techniques; our evaluation domains span sequential and non-sequential tasks with a range of behavioral models for individual users, tasks, or goal-oriented behavior. Our results demonstrate that BBCF is competitive if not better than existing methods while still offering the interpretability and explainability benefits intrinsic to many behavioral models.
A (k,n) Visual Cryptography Based on Shamir's Secret Sharing

This paper proposes a (k,n) visual cryptography based on Shamir's secret sharing (VCSSS). Using Shamir's secret sharing, a binary secret image can be shared into n meaningless shares, in order to make the generated shares more like noise images, adding the randomness within the process of sharing. Picking any k shares from n meaningless shares, the secret image can be revealed by using Lagrange interpolation. The main contribution of this paper is that applying Shamir's secret sharing to realize a (k,n) visual cryptography, and this method neither requires code book nor suffers from pixel expansion. Experimental results show VCSSS can realize a (k,n) visual cryptography, and correct decoding rate (CDR) of revealed secret image can be guaranteed.
An algebraic description of XCS

XCS and its derivatives are some of the most prominent Learning Classifier Systems (LCSs). Since XCS's design was done "algorithm first", there existed no formal basis at its inception. Over the past 20 years, several publications analysed parts of the system theoretically but the only approach to a more holistic theory of LCSs in general was never fully adapted. We present an algebraic formalisation of XCS that facilitates formal reasoning about the system and serves as a complement to earlier algorithmic descriptions. Our work is meant to give a fresh impetus to XCS and LCS theory. Since we use the programming language Haskell for our formal expressions we also describe a new abstract XCS framework in the process.
BAD: Blockchain Anomaly Detection

Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks targeting blockchain-based systems. In this paper we present BAD: the first Blockchain Anomaly Detection solution. BAD leverages blockchain meta-data, named forks, in order to collect potentially malicious activities in the network/system. BAD enjoys the following features: (i) it is distributed (thus avoiding any central point of failure), (ii) it is tamper-proof (making not possible for a malicious software to remove or to alter its own traces), (iii) it is trusted (any behavioral data is collected and verified by the majority of the network) and (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is validated via both experimental results and theoretical complexity analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.
Bluetooth Low Energy Microlocation Asset Tracking (BLEMAT) in a Context-Aware Fog Computing System

In this paper we present a Bluetooth Low Energy Microlocation Asset Tracking system (BLEMAT) that performs real-time position estimation and asset tracking based on BLE beacons and scanners. It is built on a context-aware fog computing system comprising Internet of Things controllers, sensors and a cloud platform, helped by machine-learning models and techniques. The BLEMAT system offers detecting signal propagation obstacles, performing signal perturbation correction and beacon paths exploration as well as auto discovery and onboarding of fog controller devices. These are the key characteristics of semi-supervised indoor position estimation services. In this paper we have shown there are solid basis that a fog computing system can efficiently carry out semi-supervised machine learning procedures for high-precision indoor position estimation and space modeling without the need for detailed input information (i.e. floor plan, signal propagation map, scanner position). In addition, the fog computing system inherently brings high level of system robustness, integrity, privacy and trust.