Reviewerly/exHarmony huggingface dataset:
Generating Easy-to-Understand Referring Expressions for Target Identifications

This paper addresses the generation of referring expressions that not only refer to objects correctly but also let humans find them quickly. As a target becomes relatively less salient, identifying referred objects itself becomes more difficult. However, the existing studies regarded all sentences that refer to objects correctly as equally good, ignoring whether they are easily understood by humans. If the target is not salient, humans utilize relationships with the salient contexts around it to help listeners to comprehend it better. To derive this information from human annotations, our model is designed to extract information from the target and from the environment. Moreover, we regard that sentences that are easily understood are those that are comprehended correctly and quickly by humans. We optimized this by using the time required to locate the referred objects by humans and their accuracies. To evaluate our system, we created a new referring expression dataset whose images were acquired from Grand Theft Auto V (GTA V), limiting targets to persons. Experimental results show the effectiveness of our approach. Our code and dataset are available at https://github.com/mikittt/easy-to-understand-REG.
Action and Object Interaction Recognition for Driver Activity Classification

Knowing what the driver is doing inside a vehicle is essential information for all stages of vehicle automation. For example it can be used for adaptive warning strategies in combination with an advanced driver assistance systems system, for predicting the response time to take back the control of a partially automated vehicle, or ensuring the driver is ready to manually drive a highly automated vehicle in the future. We present a system for driver activity recognition based on image sequences of an in-cabin time-of-flight camera. Our dataset includes actions such as entering and leaving a car or driver object interactions such as using a phone or drinking. In the first stage, we localize body key points of the driver. In the second stage, we extract image regions around the localized hands. These regions and the determined 3D body key points are used as the input to a recurrent neural network for driver activity recognition. With a mean average precision of 0.85 we reach better classification rates than approaches relying only on body key points or images.
Meta-learning curiosity algorithms

Exploration is a key component of successful reinforcement learning, but optimal approaches are computationally intractable, so researchers have focused on hand-designing mechanisms based on exploration bonuses and intrinsic reward, some inspired by curious behavior in natural systems. In this work, we propose a strategy for encoding curiosity algorithms as programs in a domain-specific language and searching, during a meta-learning phase, for algorithms that enable RL agents to perform well in new domains. Our rich language of programs, which can combine neural networks with other building blocks including nearest-neighbor modules and can choose its own loss functions, enables the expression of highly generalizable programs that perform well in domains as disparate as grid navigation with image input, acrobot, lunar lander, ant and hopper. To make this approach feasible, we develop several pruning techniques, including learning to predict a program's success based on its syntactic properties. We demonstrate the effectiveness of the approach empirically, finding curiosity strategies that are similar to those in published literature, as well as novel strategies that are competitive with them and generalize well.
Comparing several spectral methods used to extract displacement fields from checkerboard images

Checkerboard represents the best pattern for in-plane displacement measurement in terms of sensor noise propagation because this pattern maximizes image gradient. It also exhibits other interesting properties in terms of pattern-induced bias for instance. Digital Image Correlation (DIC) is not the best option to extract displacement fields from such periodic patterns, and spectral methods should be used instead. In this paper, it is shown that three different spectral techniques initially developed for classic bidimensional grids can be adapted to process checkerboard images. These three techniques are the Geometric Phase Analysis (GPA), the windowed version of the Geometric Phase Analysis (WGPA), and the Localized Spectrum Analysis (LSA), which can be regarded as the ultimate version of WGPA. The main features of these three techniques as well as the link between them are given in this paper. Their metrological performance are compared in terms of displacement resolution, spatial resolution and bias. Synthetic checkerboard images deformed with a suitable reference displacement field are considered for this purpose. It is shown that GPA is the fastest method. According to the metric used in this paper, the best metrological performance is obtained with WGPA with suitable settings. LSA followed by a deconvolution algorithm is just behind, but the calculation time is approximately 10 times lower than that of WGPA for the examples considered in this paper, which makes it a reasonable choice for the determination of in-plane displacement fields from checkerboard images.
Event-Driven Continuous Time Bayesian Networks

We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We demonstrate the power of the representation by applying it to model paths out of poverty for clients of CityLink Center, an integrated social service provider in Cincinnati, USA. Here the ECTBN formulation captures the effect of classes/counseling sessions on an individual's life outcome areas such as education, transportation, employment and financial education.
Empirical Bounds on Linear Regions of Deep Rectifier Networks

We can compare the expressiveness of neural networks that use rectified linear units (ReLUs) by the number of linear regions, which reflect the number of pieces of the piecewise linear functions modeled by such networks. However, enumerating these regions is prohibitive and the known analytical bounds are identical for networks with same dimensions. In this work, we approximate the number of linear regions through empirical bounds based on features of the trained network and probabilistic inference. Our first contribution is a method to sample the activation patterns defined by ReLUs using universal hash functions. This method is based on a Mixed-Integer Linear Programming (MILP) formulation of the network and an algorithm for probabilistic lower bounds of MILP solution sets that we call MIPBound, which is considerably faster than exact counting and reaches values in similar orders of magnitude. Our second contribution is a tighter activation-based bound for the maximum number of linear regions, which is particularly stronger in networks with narrow layers. Combined, these bounds yield a fast proxy for the number of linear regions of a deep neural network.
Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System

Keyword-based access to structured data has been gaining traction both in research and industry as a means to facilitate access to information.In recent years, the research community and big data technology vendors have put much effort into developing new approaches for keyword search over structured data.Accessing these data through structured query languages, such as SQL or SPARQL, can be hard for end-users accustomed to Web-based search systems.To overcome this issue, keyword search in databases is becoming the technology of choice, although its efficiency and effectiveness problems still prevent a large scale diffusion.In this work, we focus on graph data, and we propose the TSA+BM25 and the TSA+VDP keyword search systems over RDF datasets based on the ''virtual documents'' approach.This approach enables high scalability because it moves most of the computational complexity off-line and then exploits highly efficient text retrieval techniques and data structures to carry out the on-line phase.Nevertheless, text retrieval techniques scale well to large datasets but need to be adapted to the complexity of structured data.The new approaches we propose are more efficient and effective compared to state-of-theart systems.In particular, we show that our systems scale to work with RDF datasets composed of hundreds of millions of triples and obtain competitive results in terms of effectiveness.
How and What Can Humans Learn from Being in the Loop?

Abstract This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
A Benchmark and Validation of Bifacial PV Irradiance Models

In this paper, we compare the accuracy of four bifacial irradiance models used in the photovoltaic industry, by benchmarking them against field measurements. Using the bifacial irradiance modeling tools bifacial_radiance (ray tracing), bifacialvf, pvfactors and PVSyst, we modeled the front- and backside irradiance at two test sites: a fixed-tilt PV system installed in Albuquerque, NM and a single-axis tracker PV system installed in Davis, CA. We found that all four models predicted annual energy impact of bifacial gain within ±1% of one another, and the fast view-factors-based models matched the accuracy of the more computationally intensive ray tracing model. We also investigated the impact of albedo and found bifacial gain values of about 4% for the 0.16 albedo condition and about 9% for the 0.56 albedo condition for the single-axis tracking system. Our model validation work shows that pvfactors is an accurate and highly customizable solution for evaluating the front and backside irradiance in bifacial PV systems.
Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.
A Secure Identity Authentication Scheme Based on Blockchain and Identity-based Cryptography

Most blockchain-based identity authentication systems focus on using blockchain to establish the public key infrastructure (PKI). It can solve the problem of single point of failure and certificate transparency faced by traditional PKI systems, but there are still some problems such as complex certificate management and complex certificate usage process. In this paper, we propose an identity authentication scheme based on blockchain and identity-based cryptography (IBC). The scheme implements a decentralized private key generator (PKG) by deploying the smart contract in Ethereum blockchain, and uses the IBC signature algorithm and challenge-response protocol during the authentication process. Compared with other blockchain-based identity authentication systems, the scheme not only prevents the single point of failure, but also avoids the complex certificate management, has lower system complexity, and resists impersonation attack, man-in-the-middle attack and replay attack.
Quantum codes from a new construction of self-orthogonal algebraic geometry codes

We present new quantum codes with good parameters which are constructed from self-orthogonal algebraic geometry codes. Our method permits a wide class of curves to be used in the formation of these codes. These results demonstrate that there is a lot more scope for constructing self-orthogonal AG codes than was previously known.
Differentially Private Robust ADMM for Distributed Machine Learning

To embrace the era of big data, there has been growing interest in designing distributed machine learning to exploit the collective computing power of the local computing nodes. Alternating Direction Method of Multipliers (ADMM) is one of the most popular methods. This method applies iterative local computations over local datasets at each agent and computation results exchange between the neighbors. During this iterative process, data privacy leakage arises when performing local computation over sensitive data. Although many differentially private ADMM algorithms have been proposed to deal with such privacy leakage, they still have to face many challenging issues such as low model accuracy over strict privacy constraints and requiring strong assumptions of convexity of the objective function. To address those issues, in this paper, we propose a differentially private robust ADMM algorithm (PR-ADMM) with Gaussian mechanism. We employ two kinds of noise variance decay schemes to carefully adjust the noise addition in the iterative process and utilize a threshold to eliminate the too noisy results from neighbors. We also prove that PR-ADMM satisfies dynamic zero-concentrated differential privacy (dynamic zCDP) and a total privacy loss is given by $ (\epsilon, \delta)$-differential privacy. From a theoretical point of view, we analyze the convergence rate of PR-ADMM for general convex objectives, which is $\mathcal{O}(1 /K)$ with K being the number of iterations. The performance of the proposed algorithm is evaluated on real-world datasets. The experimental results show that the proposed algorithm outperforms other differentially private ADMM based algorithms under the same total privacy loss.
To Filter or Not to Filter: Measuring the Benefits of Registering in the RPKI Today

Securing the Internet’s inter-domain routing system against illicit prefix advertisements by third-party networks remains a great concern for the research, standardization, and operator communities. After many unsuccessful attempts to deploy additional security mechanisms for BGP, we now witness increasing adoption of the RPKI (Resource Public Key Infrastructure). Backed by strong cryptography, the RPKI allows network operators to register their BGP prefixes together with the legitimate Autonomous System (AS) number that may originate them via BGP. Recent research shows an encouraging trend: an increasing number of networks around the globe start to register their prefixes in the RPKI. While encouraging, the actual benefit of registering prefixes in the RPKI eventually depends on whether transit providers in the Internet enforce the RPKI’s content, i.e., configure their routers to validate prefix announcements and filter invalid BGP announcements. In this work, we present a broad empirical study tackling the question: To what degree does registration in the RPKI protect a network from illicit announcements of their prefixes, such as prefix hijacks? To this end, we first present a longitudinal study of filtering behavior of transit providers in the Internet, and second we carry out a detailed study of the visibility of legitimate and illegitimate prefix announcements in the global routing table, contrasting prefixes registered in the RPKI with those not registered. We find that an increasing number of transit and access providers indeed do enforce RPKI filtering, which translates to a direct benefit for the networks using the RPKI in the case of illicit announcements of their address space. Our findings bode well for further RPKI adoption and for increasing routing security in the Internet.
Data Mining: Min–Max Normalization Based Data Perturbation Technique for Privacy Preservation

Data mining system deals with huge volume of information which may include personal and sensitive information about the individuals such as bank credential details, financial records, health-related information, etc. Data mining process utilizes this information for analyzing purpose but privacy preservation of such sensitive data is very much crucial in data mining process in ordered to prevent the privacy about the individuals. In recent years, privacy preservation is an ongoing research topic because of the high availability of personal data which consist of private and sensitive information about the individuals. Data perturbation technique is a well-known data modification technique to preserve the privacy of sensitive values and achieves accurate data mining results. In data perturbation method, original data is perturbed (modified) before the data mining process begins. In the existing method, data modification is takes place by adding noise (Gaussian) to the original data. In this method, loss of data loss is little high, and to overcome such issue, proposed method is established. In this paper, min–max normalization-based data transformation method is used to protect the sensitive information in a dataset as well as to achieve good data mining results. The proposed method is applied on the adult dataset and the accuracy of the results is compared with Naïve Bayes classification algorithm and J48 decision tree algorithm with minimum information loss by having high data utilization. The performance of the proposed method is examined with two major considerations like maintaining the accuracy of the data mining application along with privacy preservation of original data.
Machine learning-based fall detection system for the elderly using passive RFID sensor tags

The percentage of elderly people in the world population has been rapidly increasing. Accordingly, the demand for special nursing homes and professional caregivers has also been growing to support the elderly's daily activities. Since elderly people are often unable to get up without assistance after falling, the failure to detect falling accidents can further lead to serious injuries. Hence, early fall detection is crucial to reduce the risk of the elderly's hospitalization and death caused by accidents. In order to promote early fall detection, monitoring services for elderly people based on IoT have been developed. In this paper, the proposed system uses passive RFID sensor tag is composed RFMicron's Magnus S chip, which can measure not only RSSI but also pressure. In our approach, those tags are attached to the indoor footwear and obtain a change of RSSI and pressure values during activity. Our experiment is conducted by extracting features from raw data and classifying activities with machine learning. This paper shows two training models with a different feature set developed in order to evaluate the effectiveness of passive sensor tags. Moreover, the results demonstrate the accuracy of person-dependent and person-independent with the dataset from different subjects.
On Biomedical Named Entity Recognition: Experiments in Interlingual Transfer for Clinical and Social Media Texts

Although deep neural networks yield state-of-the-art performance in biomedical named entity recognition (bioNER), much research shares one limitation: models are usually trained and evaluated on English texts from a single domain. In this work, we present a fine-grained evaluation intended to understand the efficiency of multilingual BERT-based models for bioNER of drug and disease mentions across two domains in two languages, namely clinical data and user-generated texts on drug therapy in English and Russian. We investigate the role of transfer learning (TL) strategies between four corpora to reduce the number of examples that have to be manually annotated. Evaluation results demonstrate that multi-BERT shows the best transfer capabilities in the zero-shot setting when training and test sets are either in the same language or in the same domain. TL reduces the amount of labeled data needed to achieve high performance on three out of four corpora: pretrained models reach 98–99% of the full dataset performance on both types of entities after training on 10–25% of sentences. We demonstrate that pretraining on data with one or both types of transfer can be effective.