Reviewerly/exHarmony huggingface dataset:
Designing Contracts for Trading Private and Heterogeneous Data Using a Biased Differentially Private Algorithm

Personal information and other types of private data are valuable for both data owners and institutions interested in providing targeted and customized services that require analyzing such data.In this context, privacy is sometimes seen as a commodity: institutions (data buyers) pay individuals (or data sellers) in exchange for private data.In this study, we examine the problem of designing such data contracts, through which a buyer aims to minimize his payment to the sellers for a desired level of data quality, while the latter aim to obtain adequate compensation for giving up a certain amount of privacy.Specifically, we use the concept of differential privacy and examine a model of linear and nonlinear queries on private data.We show that conventional algorithms that introduce differential privacy via zero-mean noise fall short for the purpose of such transactions as they do not provide a sufficient degree of freedom for the contract designer to negotiate between the competing interests of the buyer and the sellers.Instead, we propose a biased randomized algorithm to generate differentially private output and show that this algorithm allows us to customize the privacy-accuracy tradeoff for each individual.We use a contract design approach to find the optimal contracts when using this biased algorithm to provide privacy and show that under this combination the buyer can achieve the same level of accuracy with a lower payment as compared to using the conventional, unbiased algorithms, while at the same time incurring lower privacy loss for the sellers.
Arguing and negotiating using incomplete negotiators profiles

Computational argumentation has taken a predominant place in the modeling of negotiation dialogues over the last years. A competent agent participating in a negotiation process is expected to decide its next move taking into account an, often incomplete, model of its opponent. This work provides a complete computational account of argumentation-based negotiation under incomplete opponent profiles. After the agent identifies its best option, in any state of a negotiation, it looks for suitable arguments that support this option in the theory of its opponent. As the knowledge on the opponent is uncertain, the challenge is to find arguments that, ideally, support the selected option despite the uncertainty. We present a negotiation framework based on these ideas, along with experimental evidence that highlights the advantages of our approach.
Intrinsically Interlingual: The Wn Python Library for Wordnets

This paper introduces Wn, a new Python library for working with wordnets. Unlike previous libraries, Wn is built from the beginning to accommodate multiple wordnets — for multiple languages or multiple versions of the same wordnet — while retaining the ability to query and traverse them independently. It is also able to download and incorporate wordnets published online. These features are made possible through Wn’s adoption of standard formats and methods for interoperability, namely the WN-LMF schema (Vossen et al., 2013; Bond et al., 2020) and the Collaborative Interlingual Index (Bond et al., 2016). Wn is open-source, easily available, and well-documented.
Privacy-Preserving Machine Authenticated Key Agreement for Internet of Things

Internet of things (IoT) is the integration of computer-based systems and the physical world in which things interact with each other. Due to heterogeneity and resource-constrained feature of IoT devices, there are many privacy and security challenges resulting in many threat vulnerabilities in IoT environments. After reviewing and analyzing the recent IoT security, privacy, and authentication protocols, we will withdraw research gaps focused on the elimination of human factors in IoT authentication. In order to fill these research gaps, this paper proposes a privacy-preserving machine authenticated key agreement based on IoT, denoted as IoTMAKA. IoTMAKA uses dynamic identity and machine fingerprint to provide security and privacy. Security analysis shows that IoTMAKA provides anonymity and untraceability, provides freshness, and is secure against passive and active attacks. IoTMAKA reduces communication overheads by 20% and computational overheads by 25% on average as compared to the previous related works.
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks

Integrating external knowledge into commonsense reasoning tasks has shown progress in resolving some, but not all, knowledge gaps in these tasks.For knowledge integration to yield peak performance, it is critical to select a knowledge graph (KG) that is well-aligned with the given task's objective.We present an approach to assess how well a candidate KG can correctly identify and accurately fill in gaps of reasoning for a task, which we call KG-to-task match.We show this KGto-task match in 3 phases: knowledge-task identification, knowledge-task alignment, and knowledge-task integration.We also analyze our transformer-based KG-to-task models via commonsense probes to measure how much knowledge is captured in these models before and after KG integration.Empirically, we investigate KG matches for the SocialIQA (SIQA) (Sap et al., 2019b), Physical IQA (PIQA) (Bisk et al., 2020), and MCScript2.0(Ostermann et al., 2019) datasets with 3 diverse KGs: ATOMIC (Sap et al., 2019a), Con-ceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Koupaee and Wang, 2018).With our methods we are able to demonstrate that ATOMIC, an event-inference focused KG, is the best match for SIQA and MCScript2.0,and that the taxonomic ConceptNet and WikiHowbased KGs are the best matches for PIQA across all 3 analysis phases.We verify our methods and findings with human evaluation. 1
Research and application of semantic understanding based on Attention-RNN

Spoken language understanding module is an important part of the human-machine dialogue system, which contains two important subtasks of intent recognition and slot filling. In the current human-machine dialogue system, intent recognition and slot filling are mostly in the form of independent tasks Exist, it is difficult to further mine the latent semantic information in the text. This paper attempts to introduce attention mechanism in the process of intent recognition, and share the latent information of intent recognition and slot filling to improve the result of intent recognition and slot filling. Experiments show that the introduction of attention mechanism and the sharing of hidden information are important for slot filling and intent recognition and have a certain improvement effect.
Internet Rescue Robots for Disaster Management

The prototype development of Rescue Robot can help search and rescue teams with their difficult and lifesaving tasks.Unmanned Aerial vehicles (UAVs) and Unmanned Ground vehicles (UGVs) are viewed as two separate entities.However, upon closer examination, we can classify them into a closely-coupled system where both are just seen as separable parts of the body of one robotic entity, whose body parts can separate temporarily and acquire together again later.In this prototype, a mobile robot UGV can be used as transport and landing station for a lightweight quadcopter UAV.UAV has separable long-range vision system which provides the top view of environment for the UGV, separable images are stitched and transformed into maps and utilized for rescue operations.This rescue robot contains a combination of UGV and UAV.It is equipped with a strong manipulator arm, used for debris removal, shoring operations, and delivering medicines/ food on very rough terrain.It has light detection and ranging (lidar) sensors, cameras, enabling it to look for victims and for mapping semi-destroyed buildings.The UGV can send visual feedback to the operator at a remote location.The UAV drone has a camera for an aerial view and mapping of the place.Working in conjunction with one another and thus the human search and rescue workers, these robotic assets form a strong team, increasing the effectiveness of search and rescue operations.This project aims at the realization of a new generation of search and rescue robot which can work in semi-autonomous and wireless modes and can be used in harsh physical environments of disaster regions to hold out the given tasks more effectively by the utilization of advanced and economic sensors.the problem domain of earthquake disasters and search and rescue processes is clarified with introducing an overview of this project.Disasters unsettle the economic and social stability of society.Shortage of skilled rescue work forces, also because the risks involved in search and rescue operations, are getting foremost problems during an emergency situation.In this paper rescue robot were designed, implemented in a real time environment and analyzed the result.
Optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection

We propose an optical tensor core (OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units (DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.
Targeted Adversarial Training for Natural Language Understanding

We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding.The key idea is to introspect current mistakes and prioritize adversarial training steps to where the model errs the most.Experiments show that TAT can significantly improve accuracy over standard adversarial training on GLUE and attain new state-of-the-art zero-shot results on XNLI.Our code will be released at: https://github.com/namisan/mt-dnn.* Equal contribution.(a) BERT with standard fine-tuning (b) BERT with TAT fine-tuning
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators

Deep neural network (DNN) accelerators received considerable attention in recent years due to the potential to save energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption, however, causes bit-level failures in the memory storing the quantized weights. Furthermore, DNN accelerators are vulnerable to adversarial attacks on voltage controllers or individual bits. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, as well as random bit error training (RandBET) or adversarial bit error training (AdvBET) improves robustness against random or adversarial bit errors in quantized DNN weights significantly. This leads not only to high energy savings for low-voltage operation as well as low-precision quantization, but also improves security of DNN accelerators. In contrast to related work, our approach generalizes across operating voltages and accelerators and does not require hardware changes. Moreover, we present a novel adversarial bit error attack and are able to obtain robustness against both targeted and untargeted bit-level attacks. Without losing more than 0.8%/2% in test accuracy, we can reduce energy consumption on CIFAR10by 20%/30% for 8/4-bit quantization. Allowing up to 320 adversarial bit errors, we reduce test error from above 90% (chance level) to 26.22%.
OSINT-Based LPC-MTD and HS-Decoy for Organizational Defensive Deception

This study aimed to alleviate the theoretical limitations of existing moving target defense (MTD) and decoy concepts and improve the efficiency of defensive deception technology within an organization. We present the concept of an open-source intelligence (OSINT)-based hierarchical social engineering decoy (HS-Decoy) strategy while considering the actual fingerprint of each organization. In addition, we propose a loosely proactive control-based MTD strategy that is based on the intended competitive exposure of OSINT between defenders and attackers. Existing MTDs and decoys are biased toward proactive prevention, in that they only perform structural mutation-based attack avoidance or induce static traps. They also have practical limitations, e.g., they do not consider security characterization of each organizational social engineering attack and related utilization plans, no quantitative deception modeling is performed for the attenuation of the attack surface through exposure to OSINT, and there is no operational plan for optimal MTD and decoy application within the organization. Through the applied deception concepts proposed here, the total attack efficiency was reduced by 287% compared to the existing MTD and decoys, while the artificial deception efficiency dominated by defenders was improved by 382%. In addition, the increase rate of deception overhead was also reduced by 174%, and an optimized deceptive trade-off was also presented. In order to enable an organization to utilize the OSINT concept, statistical error reduction, and MTD mutation cycle-based deceptive selectivity, it was introduced as a loose adaptive mutation rather than a preferential avoidance strategy, and an organization-specific optimization direction was introduced through a combination of HS-Decoy and LPC-MTD. In the future, in order to improve the operational reliability of the HS-Decoy and LPC-MTD-based combined model and standardize threat information for each organization, we intend to advance it into an international standard-based complex architecture and characterize it as game theory.
Hardware Implementation of Chaotic Zigzag Map Based Bitwise Dynamical Pseudo Random Number Generator on Field-Programmable Gate Array

In this study, successful real-time implementation of discrete-time chaotic zigzag map as a Random Number Generator on field-programmable gate array (FPGA) environment is presented. For hardware implementation, in addition to ready-use circuit elements defined on 32-bit floating-point numbers, very high-speed integrated circuit hardware description language (VHDL) is used. In the scope of this study, cryptographic critical competencies such as system reliability and randomness quality related to nonlinear dynamic behaviour of zigzag map are examined. H function post - processing technique is used in the system for random numbers with low statistical quality achieved from chaotic system. Also NIST 800-22 standard test technique is used for statistical verification of bit sequences obtained from the generator. In addition to its practical applicability, the results show that the zigzag map can be used as a random number generator for embeded cryptographic applications.
SODA: Similar 3D Object Detection Accelerator at Network Edge for Autonomous Driving

Offloading the 3D object detection from autonomous vehicles to MEC is appealing because of the gains on quality, latency, and energy. However, detection requests lead to repetitive computations since the multitudinous requests share approximate detection results. It is crucial to reduce such fuzzy redundancy by reusing the previous results. A key challenge is that the requests mapping to the reusable result are only similar but not identical. An efficient method for similarity matching is needed to justify the use case. To this end, by taking advantage of TCAM's ap-proximate matching capability and NMC's computing efficiency, we design SODA, a first-of-its-kind hardware accelerator which sits in the mobile base stations between autonomous vehicles and MEC servers. We design efficient feature encoding and partition algorithms for SODA to ensure the quality of the similarity matching and result reuse. Our evaluation shows that SODA significantly improves the system performance and the detection results exceed the accuracy requirements on the subject matter, qualifying SODA as a practical domain-specific solution.
Exploring Linguistically-Lightweight Keyword Extraction Techniques for Indexing News Articles in a Multilingual Set-up.

This paper presents a study of state-of-the-art unsupervised and linguistically unsophisticated keyword extraction algorithms, based on statistic-, graph-, and embedding-based approaches, including, i.a., Total Keyword Frequency, TF-IDF, RAKE, KPMiner, YAKE, KeyBERT, and variants of TextRank-based keyword extraction algorithms. The study was motivated by the need to select the most appropriate technique to extract keywords for indexing news articles in a real-world large-scale news analysis engine. The algorithms were evaluated on a corpus of circa 330 news articles in 7 languages. The overall best F1 scores for all languages on average were obtained using a combination of the recently introduced YAKE algorithm and KPMiner (20.1%, 46.6% and 47.2% for exact, partial and fuzzy matching resp.).
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation

Learning disentangled representations of realworld data is a challenging open problem.Most previous methods have focused on either supervised approaches which use attribute labels or unsupervised approaches that manipulate the factorization in the latent space of models such as the variational autoencoder (VAE) by training with task-specific losses.In this work, we propose polarized-VAE, an approach that disentangles select attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes.We apply our method to disentangle the semantics and syntax of sentences and carry out transfer experiments.Polarized-VAE outperforms the VAE baseline and is competitive with state-of-the-art approaches, while being more a general framework that is applicable to other attribute disentanglement tasks.
IDENTIFICATION AND DETECTION OF IMMATURE WHITE BLOOD CELLS THROUGH DEEP LEARNING

Blood smears have many medical uses, including blood cell sorting, bone marrow examination, and so on. From the examination of the blood film, we can check the blood condition of the patient. In addition, it can also help doctors diagnose the patient's condition and understand the effect of treatment. Blood cell sorting is time consuming particular for immature white blood cells and it requires a lot of manpower. This paper is based on Faster R-CNN and Convolutional Neural Network (CNN) to detect and classify immature white blood cells. The test data set was obtained from Taichung Tzu Chi Hospital. In order to find the best experimental neural network structure, we have tried many different neural network architectures. A graphical user interface was created to help us identify white blood cells. Finally, cross-validation is used to increase the credibility of our experiments with an accuracy rate of nearly 90%.
An Unstructured to Structured Data Conversion using Machine Learning Algorithm in Internet of Things (IoT)

Nowadays the amount of data generated is in massive form. Continuously data is generated by the IoT sensors which is in unstructured form and this unstructured data is to be converted into structured form so that knowledge is to be extracted from it. Data generated is useful for a different type of analysis such as forecasting data that can be used for analysis for future prediction. For many organization performing analytics on such vast data become challenge. In this paper, machine learning algorithms i.e. SVM, KNN, Logistic regression, Linear Regression, and Decision tree are applied on such data and which is then converted into a structured form which helps in future prediction and compared the accuracy of each algorithm.
Efficient Security Model for RDF Files Used in IoT Applications

The openness environment of IoT ecosystem arises several security and privacy issues.However, the huge amount of data produced by several IoT devices restricts using traditional security methods.Another security challenge for IoT system is the interoperability between heterogeneous IoT devices.Semantic Web has risen as a promising technology that provides semantic annotations allowing interoperability between IoT devices.Semantic web uses RDF triples to allow semantic data exchange between heterogeneous applications.Hence, RDF files used in IoT systems require specific security mechanism that regards large data size as well as rapidly data updates.The proposed work introduces a security novel that provides RDF files with a fine grained partial encryption.The proposed method allows applying security for the sensitive parts of RDF files without affecting the public parts.Encryption metadata is stored in a container related to each individual sensitive triple.Thus accessing public data in RDF file is not affected with the encryption overheads.A motivation scenario for privacy in a smart city is used to evaluate the proposed method.Experimental results showed that the proposed methodology enhances the access time of RDF triples from 10.4 msec to 6.2 msec.Moreover the proposed method facilitates integration of separated parts of a RDF graph together.The empirical evaluation proved the enhancement in efficiency and flexibility by applying the proposed method to RDF files used in IoT systems.Moreover the insensitive triples in RDF files are not affected with the security overheads.
Improving the Reversible LSB Matching Scheme Based on the Likelihood Re-Encoding Strategy

In 2018, Tseng et al. proposed a dual-image reversible embedding method based on the modified Least Significant Bit matching (LSB matching) method. This method improved on the dual-image LSB matching method proposed by Lu et al. In Lu et al.’s scheme, there are seven situations that cannot be restored and need to be modified. Furthermore, the scheme uses two pixels to conceal four secret bits. The maximum modification of each pixel, in Lu et al.’s scheme, is two. To decrease the modification, Tseng et al. use one pixel to embed two secret bits and allow the maximum modification to decrease from two to one such that the image quality can be improved. This study enhances Tseng et al.’s method by re-encoding the modified rule table based on the probability of each hiding combination. The scheme analyzes the frequency occurrence of each combination and sets the lowest modified codes to the highest frequency case to significantly reduce the amount of modification. Experimental results show that better image quality is obtained using our method under the same amount of hiding payload.