Reviewerly/exHarmony huggingface dataset:
Basic Language Resources for 31 Languages (Plus English): The LORELEI Representative and Incident Language Packs

This paper documents and describes the thirty-one basic language resource packs created for the DARPA LORELEI program for use in development and testing of systems capable of providing language-independent situational awareness in emerging scenarios in a low resource language context. Twenty-four Representative Language Packs cover a broad range of language families and typologies, providing large volumes of monolingual and parallel text, smaller volumes of entity and semantic annotations, and a variety of grammatical resources and tools designed to support research into language universals and cross-language transfer. Seven Incident Language Packs provide test data to evaluate system capabilities on a previously unseen low resource language. We discuss the makeup of Representative and Incident Language Packs, the methods used to produce them, and the evolution of their design and implementation over the course of the multi-year LORELEI program. We conclude with a summary of the final language packs including their low-cost publication in the LDC catalog.
Knowledge Management Infrastructure Capabilities and Innovativeness of Small and Medium Scale Enterprises in Benue State, Nigeria

The contemporary business environment in Benue state, Nigeria demands all Small and Medium Scale Enterprises (SMEs) irrespective of which sector they operate in to innovate in order to secure a place in the market, and Knowledge Management (KM) infrastructure is said to be one of the strategies with which firm innovativeness can be promoted.It is in view of this that this study was conducted to examine the relationship between KM infrastructural capabilities and innovativeness of SMEs in Benue state, Nigeria.The study adopted the survey design.The population for this study consisted of 1370 employees from 14 SMEs in Benue State.The research focused only on SMEs with up to 50 employees.These SMEs were selected from the three business hubs in the state (Makurdi, Gboko and Otukpo) which covers all the geo-political zones of the State.This number cut across business activities like manufacturing, food processing, bakery, table water, schools, hoteling and ICT.Through purposive sampling technique, Five hundred and eighteen (518) employees from the selected SMEs (i.e.37 from each) made up the sample size for the study.A selfadministered-adopted questionnaire was employed to collect quantitative data for hypothesis testing.Five hundred and eighteen (518) questionnaires were issued out to the respondents however, only four hundred and forty five (445) responses validly retrieved and included in analysis.Data were statistically analyzed after being collected from the field with the aid of Statistical Package for Social Sciences (SPSS, v. 21).Pearson Correlation Analysis was employed in determining the relationship between the independent variables (technological, cultural and structural infrastructures) and the dependent variable (SMEs innovativeness).Multiple regression analysis on the other hand tested the extent of effect of independent variables on the dependent variable at 0.05 level of significance.The result of Pearson Correlation Analysis revealed that all the infrastructural dimensions (technology, culture and structure) significantly relate with SMEs Innovativeness.The result of multiple regression analysis showed a higher effect of cultural infrastructural dimension followed by technology and structure with a low effect though positive.Therefore, it is recommended among others that SMEs need to double their support for the use of technological facilities and develop the desired trainings for the operation of technological facilities.
Improving the communication and accessibility of stock assessment using interactive visualization tools

Scientists across many fields are faced with the challenge of synthesizing and communicating information from large and complex data sets. The field of stock assessment is no exception as the volume and variety of the data has grown alongside the computational methods used to integrate them. While this growth in data and model complexity has improved many stock assessments, the process of communicating key results to colleagues and stakeholders in a meaningful way has become more daunting. The traditional approach of presenting information across a series of static slides often fails to convey the richness of information available, and as such, important patterns and details are easily overlooked. Here we contend that this problem can be mediated through the effective use of new open source tools for building interactive visualizations. These tools allow a broader audience to conduct detailed explorations of the results, leading to a deeper and collective understanding of both the data and models used to inform stock assessments. As a consequence, the peer review process is more open and accessible.
Synthetic Blood Smears Generation Using Locality Sensitive Hashing and Deep Neural Networks

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by hematologists to assess some aspects of humans' health status. PBS analysis is prone to human errors and utilizing computer-based analysis can greatly enhance this process in terms of accuracy and cost. Recent approaches in learning algorithms, such as deep learning, are data hungry, but due to the scarcity of labeled medical images, researchers had to find viable alternative solutions to increase the size of available datasets. Synthetic datasets provide a promising solution to data scarcity, however, the complexity of blood smears' natural structure adds an extra layer of challenge to its synthesizing process. In this work, we propose a methodology that utilizes Locality Sensitive Hashing (LSH) to create a novel balanced dataset of 2500 synthetic blood smears. This dataset, which was automatically annotated during the generation phase, will be made public for research purposes and covers 17 essential categories of blood cells. We proved the effectiveness of the proposed dataset by utilizing it for training a deep neural network, this model got a very high accuracy score of 98.72% when tested with the well known ALL-IDB dataset. The dataset also got the approval of 5 experienced hematologists to meet the general standards of making thin blood smears.
A proactive secure file approach using a block chain technique

Many users access their information such as civil or military services and projects in an electronic way, saving both their common data as well as sensitive or personal information remotely make hundreds of attack jeopardize these information in an unauthorized way. Continuously, the attackers are working on improving developed and invisible techniques for targeting the victim, and they are taking all the methods and attempts to penetrate the network in a way that is not detected by eliminating the traces of attacks such as system log files and information related to the victim system so as not to be targeted. This paper proposed immutable and secure log files from unauthenticated access by using the blockchain technique. It will assist to provide proof of log manipulation and nonrepudiation. The results have been tested using two cases attacking the server with a blockchain and without a blockchain, the first case the log file is not altered because the attacker do not know the correct series of hash value he enters a random hash value so he is detected, second case the log file is manipulated by the attacker without being detected.
The Prototype of Thai Blockchain-based Voting System

In this paper, the prototype of Thai voting system using blockchain technology (B-VoT) has been successfully designed and developed.Hyperledger Fabric was chosen to be a major blockchain infrastructure.The web-application was developed to allow voters to vote.In addition, real-time voting results will be shown on the same website after election period has passed.We connected the web-application and blockchain database together in order to store the votes as a blockchain transaction.With our Blockchain Internet Election, the voters can easily elect on the website and their votes is automatically stored in the blockchain database as a single blockchain transaction.This voting prototype can assure the data integrity because no one can modify any information or voting results.Therefore, our system could have a huge impact on the voting reliability as well as rebuild public trustworthiness in Thailand election.
Provable secure identity-based online/offline encryption scheme with continual leakage resilience for wireless sensor network

As a potential technology, the identity-based online/offline encryption scheme is split into two phases (the offline phase and the online phase) which is especially suitable for sensor nodes with limited computation resources in that most of the works can be executed offline. However, a challenging issue is the well-known identity-based online/offline encryption schemes unable to resist continual key leakage attacks of the secret keys. To address the above security challenge, we put forth the first continual leakage-resilient identity-based online/offline encryption scheme which is suitable for ensuring secure communications in wireless sensor networks. More specifically, our formal security proofs analysis indicates that the proposed scheme can guarantee security even if partial information of the secret key is continually leaked due to side-channel attacks or fault injection attacks. Above all, compared to the existing identity-based online/offline encryption schemes, an identity-based online/offline encryption scheme with continual leakage resilient meets wireless sensor networks with strong security.
Big Data Management and Analytics in Scientific Programming: A Deep Learning-Based Method for Aspect Category Classification of Question-Answering-Style Reviews

Online product reviews are exploring on e-commerce platforms, and mining aspect-level product information contained in those reviews has great economic benefit. The aspect category classification task is a basic task for aspect-level sentiment analysis which has become a hot research topic in the natural language processing (NLP) field during the last decades. In various e-commerce platforms, there emerge various user-generated question-answering (QA) reviews which generally contain much aspect-related information of products. Although some researchers have devoted their efforts on the aspect category classification for traditional product reviews, the existing deep learning-based approaches cannot be well applied to represent the QA-style reviews. Thus, we propose a 4-dimension (4D) textual representation model based on QA interaction-level and hyperinteraction-level by modeling with different levels of the text representation, i.e., word-level, sentence-level, QA interaction-level, and hyperinteraction-level. In our experiments, the empirical studies on datasets from three domains demonstrate that our proposals perform better than traditional sentence-level representation approaches, especially in the Digit domain.
Efficient Medical Instrument Detection in 3D Volumetric Ultrasound Data

Ultrasound-guided procedures have been applied in many clinical therapies, such as cardiac catheterization and regional anesthesia.Medical instrument detection in 3D Ultrasound (US) is highly desired, but the existing approaches are far from real-time performance.Our objective is to investigate an efficient instrument detection method in 3D US for practical clinical use.We propose a novel Multi-dimensional Mixed Network for efficient instrument detection in 3D US, which extracts the discriminating features at 3D full-image level by a 3D encoder, and then applies a specially designed dimension reduction block to reduce the spatial complexity of the feature maps by projecting from 3D space into 2D space.A 2D decoder is adopted to detect the instrument along the specified axes.By projecting the predicted 2D outputs, the instrument is detected or visualized in the 3D volume.Furthermore, to enable the network to better learn the discriminative information, we propose a multi-level loss function to capture both pixel-and image-level differences.We carried out extensive experiments on two datasets for two tasks: (1) catheter detection for cardiac RF-ablation and (2) needle detection for regional anesthesia.Our experiments show that our proposed method achieves a detection error of 2-3 voxels with an efficiency of about 0.12 sec per 3D US volume.The proposed method is 3-8 times faster than the state-of-the-art methods, leading to real-time performance.The results show that our proposed method has significant clinical value for real-time 3D US-guided intervention.
A dynamic taint tracking optimized fuzz testing method based on multi-modal sensor data fusion

Abstract The safety of the Industrial Internet Control Systems has been a hotspot in the information security. To meet the needs of communication, a large variety of proprietary protocols have emerged in the field of industrial control. The protocol field is often trusted in the implementation of industrial control terminal code. If attackers modify the data of these fields using the protocol defect, the operation of the program can be controlled and the entire system will be affected. To cope with such security threats, academia and industry generally adopt fuzz test methods. However, the current industrial control protocol fuzz test methods generally have low code coverage, where unified description models are missing and test cases are not targeted. A method of fuzzification processing combined with dynamic multi-modal sensor communication data is proposed. To track the program execution, the dynamic pollution analysis is used to search for the input fields that affect the execution of the conditional branch and capture the dependencies between the conditional branches to guide the grammar generation of test cases, which can increase the chances of executing deep code. The experimental results show that the proposed method improves the validity and code coverage of test cases to a certain extent and greatly increases the probability of anomaly detection in the protocol implementation.
Fly-Crash-Recover: A Sensor-based Reactive Framework for Online Collision Recovery of UAVs

Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular thanks to the multiplicity of operations in which they can be deployed such as surveillance, search and rescue, mapping, transportation, hobby and recreational activities. Although sensors like LIDARs and cameras are often present on such systems for motion planning to avoid obstacles, collisions can still occur in very dense and unstructured environments, especially if disturbances are present. In this work, we research techniques to recover UAVs after a collision has occurred. We note that the on-board sensors, especially the inertial sensor used to stabilize the UAV, run at a high frequencies obtaining hundreds of data points every second. At run-time, this can be leveraged at the moment of a collision to quickly detect and recover the system. Our approach considers knowledge of UAV system dynamics to predict the expected behavior of the vehicle under safe flight conditions and leverage such expectations together with inertial data to detect collisions rapidly (on the order of milliseconds). We also propose a potential field-based approach to map the collision and create the correct reactive maneuver to avoid the collided object and bring the system back to a stable and safe configuration. Experiments are executed using ROS on two micro-quadrotor UAV platforms having different dynamics and performances, while colliding with poles and walls positioned in different configurations. In our results, we are able to show that the UAVs are successfully able to detect and avoid a collision, while also providing a rigorous analysis of the conditions in which the system can recover from imminent collisions.
Poster Abstract: Passive Activity Classification of Smart Homes through Wireless Packet Sniffing

Network communications, despite being encrypted, leak crucial information via side channels. WiFi networks are more prone to such side-channel attacks since any attacker within the network’s range can passively eavesdrop the channel. With the increasing number of smart home devices and sensors connecting to private WiFi networks, it is essential to understand the inadvertent information leakage through WiFi side-channels. Our work demonstrates how fine-granular information on the activities happening inside a house can be inferred by passively monitoring WiFi network traffic. In particular, we were able to correctly classify various user interactions with simple IoT devices such as smart bulbs or power sockets as well as advanced voice-based intelligent assistants.
Learning Representations that Support Extrapolation

Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, temporal context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.
Pipeline for Real-Time Video View Synthesis

Immersive experiences of real captured scenes are still challenging due to the amount of data and computational complexity required to achieve real-time. Indeed, Light Field (LF) videos are the required input to produce the sense of depth thanks to the motion parallax, but manipulating such multiview content is cumbersome and there is a lack of end-to-end pipelines that effectively edit and render such content.In this work, we propose a compact representation for LF videos in the form of an atlas. We provide the detailed pipeline to compute the atlas from captured LFs and we demonstrate its efficiency for real-time view synthesis with limited computer power requirements. Finally, we demonstrate how our atlases can be merged to produce real-time fusion of LF videos.
QuAChIE: Question Answering based Chinese Information Extraction System

In this paper, we present the design of QuAChIE, a Question Answering based Chinese Information Extraction system. QuAChIE mainly depends on a well-trained question answering model to extract high-quality triples. The group of head entity and relation are regarded as a question given the input text as the context. For the training and evaluation of each model in the system, we build a large-scale information extraction dataset using Wikidata and Wikipedia pages by distant supervision. The advanced models implemented on top of the pre-trained language model and the enormous distant supervision data enable QuAChIE to extract relation triples from documents with cross-sentence correlations. The experimental results on the test set and the case study based on the interactive demonstration show its satisfactory Information Extraction quality on Chinese document-level texts.
Low-Rank Bottleneck in Multi-head Attention Models

Attention based Transformer architecture has enabled significant advances in the field of natural language processing. In addition to new pre-training techniques, recent improvements crucially rely on working with a relatively larger embedding dimension for tokens. Unfortunately, this leads to models that are prohibitively large to be employed in the downstream tasks. In this paper we identify one of the important factors contributing to the large embedding size requirement. In particular, our analysis highlights that the scaling between the number of heads and the size of each head in the current architecture gives rise to a low-rank bottleneck in attention heads, causing this limitation. We further validate this in our experiments. As a solution we propose to set the head size of an attention unit to input sequence length, and independent of the number of heads, resulting in multi-head attention layers with provably more expressive power. We empirically show that this allows us to train models with a relatively smaller embedding dimension and with better performance scaling.
Consistent Domain Structure Learning and Domain Alignment for 2D Image-Based 3D Objects Retrieval

2D image-based 3D objects retrieval is a new topic for 3D objects retrieval which can be used to manage 3D data with 2D images. The goal is to search some related 3D objects when given a 2D image. The task is challenging due to the large domain gap between 2D images and 3D objects. Therefore, it is essential to consider domain adaptation problems to reduce domain discrepancy. However, most of the existing domain adaptation methods only utilize the semantic information from the source domain to predict labels in the target domain and neglect the intrinsic structure of the target domain. In this paper, we propose a domain alignment framework with consistent domain structure learning to reduce the large gap between 2D images and 3D objects. The domain structure learning module makes use of both the semantic information from the source domain and the intrinsic structure of the target domain, which provides more reliable predicted labels to the domain alignment module to better align the conditional distribution. We conducted experiments on two public datasets, MI3DOR and MI3DOR-2, and the experimental results demonstrate the proposed method outperforms the state-of-the-art methods.
Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN

Moving object detection and tracking from image sequences has been extensively studied in a variety of fields. Nevertheless, observing geometric attributes and identifying the detected objects for further investigation of moving behavior has drawn less attention. The focus of this study is to determine moving trajectories, object heights, and object recognition using a monocular camera configuration. This paper presents a scheme to conduct moving object recognition with three-dimensional (3D) observation using faster region-based convolutional neural network (Faster R-CNN) with a stationary and rotating Pan Tilt Zoom (PTZ) camera and close-range photogrammetry. The camera motion effects are first eliminated to detect objects that contain actual movement, and a moving object recognition process is employed to recognize the object classes and to facilitate the estimation of their geometric attributes. Thus, this information can further contribute to the investigation of object moving behavior. To evaluate the effectiveness of the proposed scheme quantitatively, first, an experiment with indoor synthetic configuration is conducted, then, outdoor real-life data are used to verify the feasibility based on recall, precision, and F1 index. The experiments have shown promising results and have verified the effectiveness of the proposed method in both laboratory and real environments. The proposed approach calculates the height and speed estimates of the recognized moving objects, including pedestrians and vehicles, and shows promising results with acceptable errors and application potential through existing PTZ camera images at a very low cost.