Reviewerly/exHarmony huggingface dataset:
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data

Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection considering the imbalance of normal situations: In particular, driving data consists of multiple normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous ones. Existing machine learning based anomaly detection approaches do not fare sufficiently well when applied to such imbalanced data. In this paper, we present a novel multi-task learning (LSTM autoencoder and predictor) based approach that leverages domain-knowledge (maneuver labels) for anomaly detection in driving data. We evaluate the proposed approach both quantitatively and qualitatively on 150 hours of real-world driving data and show improved performance over baseline/existing approaches.
A Framework for Word Embedding Based Automatic Text Summarization and Evaluation

Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and effective decision. However, how much meaning of the source text can be preserved is becoming harder to evaluate. The most commonly used automatic evaluation metrics like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) strictly rely on the overlapping n-gram units between reference and candidate summaries, which are not suitable to measure the quality of abstractive summaries. Another major challenge to evaluate text summarization systems is lack of consistent ideal reference summaries. Studies show that human summarizers can produce variable reference summaries of the same source that can significantly affect automatic evaluation metrics scores of summarization systems. Humans are biased to certain situation while producing summary, even the same person perhaps produces substantially different summaries of the same source at different time. This paper proposes a word embedding based automatic text summarization and evaluation framework, which can successfully determine salient top-n sentences of a source text as a reference summary, and evaluate the quality of systems summaries against it. Extensive experimental results demonstrate that the proposed framework is effective and able to outperform several baseline methods with regard to both text summarization systems and automatic evaluation metrics when tested on a publicly available dataset.
A method of reactive control for 3D navigation of a nonholonomic robot in tunnel-like environments

A nonholonomic under-actuated robot with a bounded control range travels inside an unknown and unstructured 3D tunnel bounded by a generic 2D surface. The robot has access to the nearest point of the surface and measures the distance to the surface along any ray from a narrow beam emitted from the robot around the direction to the nearest point. We present a new navigation law that ensures constant advancement of the robot through the tunnel, along with respecting a given safety margin to its surface, driving the robot to the desired distance to it, and subsequently maintaining this distance. This law is reactive: it generates the current control as a reflex-like reaction to the current observation. Rigorous justification of the proposed guidance approach is provided; its applicability and performance are confirmed by computer simulation tests.
Informing Makerspace Outcomes Through a Linguistic Analysis of Written and Video-Recorded Project Assessments

A growing body of research focuses on what outcomes to assess in makerspaces, and appropriate formats for capturing those outcomes (e.g. reflections, surveys, and portfolios). Linguistic analysis as a data mining technique holds promise for revealing different dimensions of learning exhibited by students in makerspaces. In this study, student reflections on makerspace projects were gathered in 2 formats over 2 years: private written assessments captured in the 3D GameLab gamification platform, and semi-public video-recorded assessments posted in the more social FlipGrid platform. Transcripts of student assessments were analyzed using Linguistic Inquiry Word Count (LIWC) to generate 4 summary variables thought to inform makerspace outcomes of interest (i.e. analytical thinking, authenticity, clout, and emotional tone). Comparative findings indicate that written assessments may elicit more analytical thinking about maker projects compared with less analytical conversation in videos, while video assessments may elicit somewhat higher clout scores as evidence of social scaffolding along with a much more positive emotional tone. Recommendations are provided for layering assessment approaches to maximize the potential benefits of each format, including reflective writing for social spaces, in social groups, and about design processes and procedures.
A Sensor based Secured Health Monitoring and Alert Technique using IoMT

A reliable health monitoring system essentially requires real time and continuous monitoring of critical health parameters of the patient and a prognostic approach to generate and send an alert during medical emergency. This paper proposes a sensor based health monitoring technique with adequate security features using Huffman Compression Coding and Rivest Shamir Adleman (RSA) encryption technique. Further, based on a prognostic approach an alert mechanism is developed with embedded software and internet of things. This proposed system is an improved version of existing systems and can be useful for patients on move or in intensive care unit.
A Survey on Quantum Key Distribution

Quantum key distribution is a catchword among industry specialist now a day.It is arising an alarming situation to all the current network security techniques. These are the basic properties of Quantum Mechanics, which makes a combination with network security. At present there are so many companies out there in market which are making progress it this fields day by day.Quantum cryptography is a very important technology now days as it is helping a lot to secure the future network communication. This particular field has gained the attention from industry as well as from academics. Here we are writing this paper to provide the general review of various fields belongs to Quantum Computing, but our main aim is to provide a brief review and analysis of recent development in the field of Quantum Key Distribution, most famous and developed field of Quantum Computing. This paper presents review of Quantum Computing including different application fields like Quantum public key cryptography, QKD, Quantum Authentication. First we discuss the basics of Quantum Cryptography then we discuss the definition of Quantum key Distribution, Various protocols of this field. Then after we discuss the opportunities in this field.
Dual Adversarial Learning Based Network Alignment

Network alignment, which aims to learn a matching between the same entities across multiple information networks, often suffers challenges from feature inconsistency, high-dimensional features, to unstable alignment results. This paper presents a novel network alignment framework, RANA, that combines dual generative adversarial network (GAN) techniques to match the distributions of two networks based on two dimensions of distance and shape. First, we propose an adversarial network distribution matching model to perform the bidirectional cross-network alignment translations between two networks, such that the cross-network transformed distributions of two networks move closer to each other and finally meet with each other halfway. In addition, a homophily consistency loss is introduced to maintain the vertex homophily consistency between pairwise vertices on two networks in both the embedding space. Second, in order to address the feature inconsistency issue, we integrate a dual adversarial autoencoder module with an adversarial two-class classification model together to twist the cross-network transformed distributions of two networks, such that two distributions could have the same shape. This facilitates the translations of the distributions of two networks in the adversarial network distribution matching model. Moreover, a semantic preservation loss is introduced to preserve the original embedding semantics of one network when this network is translated to another network and returned to itself. Third but last, the competition game by integrating the above two adversarial models together can help project two copies of the same vertices with high-dimensional inconsistent features into the same low-dimensional embedding space, and thus guarantee the distribution consistency between two networks in terms of both distance and shape.
Semantic similarity and text summarization based novelty detection

Current web crawlers search the queries at very high speed, but the problem of novelty detection or redundant information still persists. It consumes precious time and memory of users in search of the new document over the internet. In this paper, an innovative novelty detection mechanism is proposed, which can be appended with the current web crawlers. The proposed mechanism first summarizes the text, based on ontology, and from the obtained summary, semantic similarity is calculated using word net 3.0. The hash value is then calculated using the winnowing algorithm. This hash value of the document is matched with others using the Dice coefficient to calculate the similarity index. Based on the threshold chosen for similarity, the document is treated either as novel or not. This proposed mechanism is implemented using SQL as backend and visual studio-2012 as frontend. The results show that the projected strategy not only reduces memory consumption but also decreases the number of resultant documents, hence minimize the user time for searching the data from the obtained results. Also, the proposed approach can be used with other search engines like Google, Yahoo, Bing, and Alta Vista to minimize superfluous documents.
Beyond Geo-First Law: Learning Spatial Representations via Integrated Autocorrelations and Complementarity

Spatial representation learning (SRL) is to automatically learn feature representations that characterize spatial entities. In this paper, we study the problem of improving spatial representation learning using spatial structure knowledge. We consider two types of structure knowledge: (1) spatial autocorrelations refer to the pattern that similar spatial entities are more likely to share similar roles and configurations. (2) spatial complementarity refers to the effect that the role of a spatial entity can be complemented and augmented by other different yet compatible spatial entities. Along this line, we develop a step-by-step SRL framework to integrate spatial autocorrelations and complementarity. This framework includes four testable steps. First, we construct multi-view POI-POI(Point of Interest) graphs to characterize the static and dynamic patterns of each spatial region. We then use the graphs as inputs to train an adversarial autoencoder (AAE) that can preserve the spatial autocorrelation property and learn representations of spatial entities. Later, with the learned representations extracted from AAE inputs, a Graph Convolutional Network (GCN) is trained in an unsupervised fashion in order to overcome label sparsity and capture the spatial complementarity effect. In this way, we significantly improve the quality of spatial representations. In addition, we apply the proposed method to characterize residential communities for predicting real estate prices. Finally, we present intensive experimental results with real-world real estate data to demonstrate the proposed method effectiveness.
Training Full-Page Handwritten Text Recognition Models without Annotated Line Breaks

Training Handwritten Text Recognition (HTR) models typically requires large amounts of labeled data which often are line or page images with corresponding line-level ground truth (GT) transcriptions. Many digital collections have page-level transcriptions for each image, but the transcription is unformatted, i.e., line breaks are not annotated. Can we train lined-based HTR models using such data? In this work, we present a novel alignment technique for segmenting page-level GT text into text lines during HTR model training. This text segmentation problem is formulated as an optimization problem to minimize the cost of aligning predicted lines with the GT text. Using both simulated and HTR model predictions, we show that the alignment method identifies line breaks accurately, even when the predicted lines have high character error rates (CER). We removed the GT line breaks from the ICDAR-2017 READ dataset and trained a HTR model using the proposed alignment method to predict line breaks on-the-fly. This model achieves comparable CER w.r.t. to the same model trained with the GT line breaks. Additionally, we downloaded an online digital collection of 50K English journal pages (not curated for HTR research) whose transcriptions do not contain line breaks, and achieve 11% CER.
Chaos-Based Random Sampling for Photometric Invariant Shoe Detection With Vision Sensor in Human–Robot Coexisting Environments

People-following in a leader-follower scheme is considered as a major contemporary research problem in the domain of human-robot coexistence. The problem essentially requires human detection and tracking, and, in recent times, vision sensing based shoe detection has evolved as an effective mechanism for human detection. The problem gets more challenging when the environment becomes affected by photometric conditions like varying illumination, shadows, specularities, etc. Recently, a state-of-the-art fast image template matching algorithm, called photometric-invariant CFAsT-match (PICFAsT-match), has been specifically proposed for shoe detection based people-following in those challenging environments. In this approach, for determining the best shoe matching result corresponding to a frame, each single transformation included in a number of specified grids of general affine transformations is evaluated. However, in order to achieve a high speed real-life implementation of PICFAsT-match, every transformation is assessed by considering a handful number of pixels sampled randomly from the images. Research beyond PICFAsT-match reveals that the matching accuracy significantly relies on the quality of the random sampling. In this paper, we propose an improved variant of PICFAsT-match, called chaos based PICFAsT-match (CBPICFAsT-match), where a novel approach for this process of random sampling of pixels has been developed. This strategy is based on the state-time histories of multiple fractional order chaotic systems. Experimental results aptly demonstrate the superiority of the proposed algorithm, when compared to competing state-of-the-art algorithms, successfully employed for the said shoe detection purpose in human-robot coexisting environments.
ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records

In this paper, we present a large historical database of Chinese family records with the aim to develop robust systems for historical document analysis. In this direction, we propose a Historical Document Reading Challenge on Large Chinese Structured Family Records (ICDAR 2019 HDRC-CHINESE). The objective of the competition is to recognize and analyze the layout, and finally detect and recognize the textlines and characters of the large historical document image dataset containing more than 100000 pages. Cascade R-CNN, CRNN, and U-Net based architectures were trained to evaluate the performances in these tasks. Error rate of 0.01 has been recorded for textline recognition (Task1) whereas a Jaccard Index of 99:54% has been recorded for layout analysis (Task2). The graph edit distance based total error ratio of 1:5% has been recorded for complete integrated textline detection and recognition (Task3).
White Mica Geochemistry: Discriminating Between Barren and Mineralized Porphyry Systems

Abstract The Grasshopper prospect, located 23 km west-southwest from Dillon, Montana, presents exposed zones of phyllic alteration assemblages comprising the early and late phyllic styles. The mineral chemistry of white micas from both phyllic alteration zones was evaluated by short-wave infrared spectroscopy, electron microprobe analysis, and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). The early phyllic expression consists of white to green micas characterized by longer Al-OH absorption wavelengths (2,204–2,210 nm), whereas the late phyllic phase contains white micas with shorter Al-OH absorption wavelengths (2,197–2,204 nm). Correlation with electron microprobe data found that the Tschermak substitution in the white micas is mainly controlled by Mg concentrations. Based on LA-ICP-MS data, higher Mn and Sr concentrations characterize white micas from the early phyllic alteration, whereas higher concentrations of B, Ba, Cr, Cs, Cu, Li, Rb, Sc, Sn, Ti, Tl, V, and W are present in white micas from the late phyllic style. Systematic zoning patterns of trace element concentrations in white micas from the early and late phyllic alteration styles were confirmed at Grasshopper. In general, increasing trends toward the center of the system were observed in V, Cu, Sc, Sn, W, and Zn, whereas increasing trends outward from the hydrothermal center were reported in Li and Cs. Comparison of the trace element concentrations of white micas from the early phyllic style from the barren system of Grasshopper and the mineralized system of Copper Cliff indicates significant differences in Zn, Cr, B, Tl, Sn, and Cs. Therefore, we propose a preliminary discrimination (Zn + Cr + B vs. Tl + Sn + Cs) plot that can be used to differentiate white micas from the early phyllic alteration among mineralized and weakly to unmineralized systems.
Measuring Burstiness in Data Center Applications

Buffer sizing is a tricky task --- it depends on a large number of variables, ranging from congestion control to traffic engineering. Still, the most unpredictable contributors are the workloads running in the network. The link utilization and burstiness of these workloads dictate the buffer depth needed by a switch. But what is a burst? Do traditional definitions still apply in the age in which switches transfer terabits of data and billions of packets every second? Unless we assess bursts correctly, we are unlikely to size buffers appropriately. In this work, we present a measurement-led evaluation of the burstiness of different data center applications. We address the question of "what is a burst?" and assert that common techniques cannot answer this question in modern data centers. We quantify the change in burstiness of the studied applications across multiple vectors, including latency and network perspective, and generalize our results to the common case. Our observations can inform future buffer sizing efforts and guide switch configurations. Our dataset is openly available for the benefit of the community.
Transfer learning for Turkish named entity recognition on noisy text

Abstract In this article, we investigate using deep neural networks with different word representation techniques for named entity recognition (NER) on Turkish noisy text. We argue that valuable latent features for NER can, in fact, be learned without using any hand-crafted features and/or domain-specific resources such as gazetteers and lexicons. In this regard, we utilize character-level, character n-gram-level, morpheme-level, and orthographic character-level word representations. Since noisy data with NER annotation are scarce for Turkish, we introduce a transfer learning model in order to learn infrequent entity types as an extension to the Bi-LSTM-CRF architecture by incorporating an additional conditional random field (CRF) layer that is trained on a larger (but formal) text and a noisy text simultaneously. This allows us to learn from both formal and informal/noisy text, thus improving the performance of our model further for rarely seen entity types. We experimented on Turkish as a morphologically rich language and English as a relatively morphologically poor language. We obtained an entity-level F1 score of 67.39% on Turkish noisy data and 45.30% on English noisy data, which outperforms the current state-of-art models on noisy text. The English scores are lower compared to Turkish scores because of the intense sparsity in the data introduced by the user writing styles. The results prove that using subword information significantly contributes to learning latent features for morphologically rich languages.
Audita: A Blockchain-based Auditing Framework for Off-chain Storage

The cloud changed the way we manage and store data. Today, cloud storage services offer clients an infrastructure that allows them a convenient source to store, replicate, and secure data online. However, with these new capabilities also come limitations, such as lack of transparency, limited decentralization, and challenges with privacy and security. And, as the need for more agile, private and secure data solutions continues to grow exponentially, rethinking the current structure of cloud storage is mission-critical for enterprises. By leveraging and building upon blockchain's unique attributes, including immutability, security to the data element level, distributed (no single point of failure), we have developed a solution prototype that allows data to be reliably stored while simultaneously being secured, with tamper-evident auditability, via blockchain. The result, Audita, is a flexible solution that assures data protection and solves challenges such as scalability and privacy. Audita works via an augmented blockchain network of participants that include storage-nodes and block-creators. In addition, it provides an automatic and fair challenge system to assure that data is distributed and reliably and provably stored.
A Simultaneous Approach for Compression and Encryption Techniques Using Deoxyribonucleic Acid

The Data Compression is a creative skill which defined scientific concepts of providing contents in a compact form. Thus, it has turned into a need in the field of communication as well as in different scientific studies. Data transmission must be sufficiently secure to be utilized in a channel medium with no misfortune; and altering of information. Encryption is the way toward scrambling an information with the goal that just the known receiver can peruse or see it. Encryption can give methods for anchoring data. Along these lines, the two strategies are the two crucial advances that required for the protected transmission of huge measure of information. In typical cases, the compacted information is encoded and transmitted. In any case, this sequential technique is time consumption and computationally cost. In the present paper, an examination on simultaneous compression and encryption technique depends on DNA which is proposed for various sorts of secret data. In simultaneous technique, both techniques can be done at single step which lessens the time for the whole task. The present work is consisting of two phases. First phase, encodes the plaintext by 6-bits instead of 8-bits, means each character represented by three DNA nucleotides whereas to encode any pixel of image by four DNA nucleotides. This phase can compress the plaintext by 25% of the original text. Second phase, compression and encryption has been done at the same time. Both types of data have been compressed by their half size as well as encrypted the generated symmetric key. Thus, this technique is more secure against intruders. Experimental results show a better performance of the proposed scheme compared with standard compression techniques.