Reviewerly/exHarmony huggingface dataset:
How Distance-Bounding Can Detect Internet Traffic Hijacking

We propose a two-party cryptographic protocol for detecting traffic hijacking over the Internet. Our proposal relies on a distance-bounding mechanism that measures the round-trip time of packets to decide whether an attack is ongoing. The protocol requires only two cryptographic operations per execution which leads to very few additional workload for the users. We demonstrate the efficiency of the protocol using large-scale experiments and we discuss the choice of the decision function w.r.t. the false positive and negative cases.
Model-Based Risk Analysis Approach for Network Vulnerability and Security of the Critical Railway Infrastructure

This study focuses on threat modeling, vulnerability analysis, and risk management within the critical railway transportation infrastructure. The Railway Transportation System is a highly complex, national critical infrastructure and its cybersecurity evaluation is crucial, but is still an extremely hard problem. In this paper, a novel threat modeling and risk management approach using a domain-specific modeling environment is presented. Two risk analysis techniques based on attack trees are developed to systematically model the potential risks in a cyber-physical system and provide quantitative analysis of the vulnerabilities. The automated risk assessment tool can prioritize component level vulnerabilities for potential mitigation actions. A scenario language and associated tools in the framework allow modeling and evaluation of cyber-games using a library of system exploits and mitigation actions. Cyber-games enable assessment of system-level risks and development of comprehensive risk management plans. Another key capability is the handling of dynamic network connections with variable vulnerability propagation in railway communication networks where locomotives and its devices are mobile. These capabilities are demonstrated with a case study in the railway transportation domain.
Arduino Based Smart Glove for Visually Impaired

There are around 37 million individuals across the globe who are visually impaired, more than 15 million are from India. Individuals with visual incapacities use Braille language for perusing, composing and for correspondence reason and frequently subject to outside help for explore around places. Notwithstanding, the greatest test for blind individuals is to wander around the spots with next to no predefined help. These individuals need to live freely with top caliber and secure way of life in the general public. As the innovation developing quickly and headway in the sensor revolution has driven the stage to carry out a smart peripheral for the need of outwardly debilitated to leave the human help. Hence, in this work a smart glove for visually impaired people has been designed based on Arduino. The proposed smart glove has a capability of detecting obstacles and improves the confidence of blind people when they use to walk in the public places. It has an ultrasonic sensor which sense the obstacles, and object motion appeared Infront of the visually impaired people and has an output devise such as; vibrating motor, buzzer and LED indication for identifying the mobility of the objects through the sound. The proposed smart glove is apt for blind people only because they should sense the vibration and continuous beep sound from buzzer in order identify the object mobility.
A PHD Filter Based Localization System for Robotic Swarms

In this paper, we present a Probability Hypothesis Density (PHD) filter based relative localization system for robotic swarms. The system is designed to use only local information collected by onboard lidar and camera sensors to identify and track other swarm members within proximity. The multi-sensor setup of the system accounts for the inability of single sensors to provide enough information for the simultaneous identification of teammates and estimation of their position. However, it also requires the implementation of sensor fusion techniques that do not employ complex computer vision or recognition algorithms, due to robots’ limited computational capabilities. The use of the PHD filter is fostered by its inherent multi-sensor setup. Moreover, it aligns well with the overall goal of this localization system and swarm setup that does not require the association of a unique identifier to each team member. The system was tested on a team of four robots.
A data driven approach for Sanskrit Derivative Nouns analysis

An important concept in Sanskrit is that of Pratyaya (suffix) and pada (inflectional words formed due to suffixes). Two of the most important types of Pratyaya are Kridanta and Taddhitanta. Kridanta and Taddhitanta are types of derivative nouns and are critical to morphological analysis of Sanskrit texts. In this work, we present a benchmark corpus called Pratyaya-Kosh to evaluate the performance of Kridanta and Taddhitanta analysis tools. We also present our own neural network based approach for derivative nouns analysis while evaluating the same against most prominent Sanskrit Morphological Analysis tools.
A Study of Levenshtein Transformer and Editor Transformer Models for Under-Resourced Languages

Transformers are the current state-of-the-art type of neural network model for dealing with sequences. Evidently, the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. Recently, transformer-based models such as the Edit-Based Transformer with Repositioning (EDITOR) and Levenshtein Transformer (LevT) models have become popular in neural machine translation. To the best of our knowledge, there are no experiments for these two models using under-resourced languages. In this paper, we compared the performance and decoding time of the EDITOR model and the LevT model. We conducted the experiments for under-resourced language pairs, namely, Thai-to-English, Thai-to-Myanmar, English-to-Myanmar, and vice versa. The experimental results showed that the EDITOR model outperforms the LevT model in English-Thai, Thai-English and English-Myanmar language pairs whereas LevT achieves better score than EDITOR in Thai-Myanmar, Myanmar-Thai and Myanmar-English language pairs. Regarding the decoding time, EDITOR model is generally faster than the LevT model in the four language pairs. However, in the case of English-Myanmar and Myanmar-English pairs, the decoding time of EDITOR is slightly slower than the LevT model. At last, we investigated the system level performance of both models by means of compare-mt and word error rate (WER).
MODES OF CONVERGENCE TO THE TRUTH: STEPS TOWARD A BETTER EPISTEMOLOGY OF INDUCTION

Abstract Evaluative studies of inductive inferences have been pursued extensively with mathematical rigor in many disciplines, such as statistics, econometrics, computer science, and formal epistemology. Attempts have been made in those disciplines to justify many different kinds of inductive inferences, to varying extents. But somehow those disciplines have said almost nothing to justify a most familiar kind of induction, an example of which is this: “We’ve seen this many ravens and they all are black, so all ravens are black.” This is enumerative induction in its full strength. For it does not settle with a weaker conclusion (such as “the ravens observed in the future will all be black”); nor does it proceed with any additional premise (such as the statistical IID assumption). The goal of this paper is to take some initial steps toward a justification for the full version of enumerative induction, against counterinduction, and against the skeptical policy. The idea is to explore various epistemic ideals, mathematically defined as different modes of convergence to the truth, and look for one that is weak enough to be achievable and strong enough to justify a norm that governs both the long run and the short run. So the proposal is learning-theoretic in essence, but a Bayesian version is developed as well.
Learning Personalized End-to-End Task-Oriented Dialogue for Fast and Reliable Adaptation

Personalized task-oriented dialog agents can select responses according to the personalities of users. In this way, they facilitate understanding, and improve the efficiency of the conversation. Existing personalized agents generate matching functions according to human designed persona descriptions. This agent works well in the traditional scenario where sufficient samples are used, but can hardly fast adapt to new personas with few samples. In this paper, we propose to extend meta-learning algorithms to personalized end-to-end task-oriented dialogue learning, and train a switch model to allow for human agent use. Our model learns to quickly adapt to new personas leveraging a few dialogue samples and requesting human agents, while maximizing the task success of users. Empirical results on the Personalized bAbI dataset indicate that our proposal is effective in achieving the desired goals.
Detecting Gradual Structure Changes of Non-parametric Distributions via Kernel Complexity

This study is concerned with an algorithm to detect structural change with KC in nonparametric distributions. We propose an index of the quantification of structural information for nonparametric distributions with the aim of detecting their changes in time series data. In a parametric model such as a Gaussian mixture model, the number of clusters can represent the structural information. However, the notion of structural information for modeling data nonparametrically does not exist. Herein we introduce the novel notion of kernel complexity (KC) as structural information in a nonparametric setting. The key idea of KC is to combine the information bias by the Gini index with the quantity of information measured by the normalized maximum likelihood (NML) code length. We empirically show the similarities between KC and the number of clusters in a parametric model under certain conditions. Using synthetic and real datasets, we empirically demonstrate that our framework enables us to detect the structural changes underlying the data. We use the synthetic datasets to demonstrate the usefulness of our method with some characteristic data distribution. We then use real datasets to evaluate the validity of the detected results both quantitatively and qualitatively.
Negative Siamese Network for Classifying Semantically Similar Sentences

In semantically similar sentence-level classification, BERT often neglects the subtle semantic differences in sentences and performs poorly because of its inherent anisotropy regarding semantic space. This paper explores methods to enlarge the space distance between labels assigned to different sentences. First, the sentence embedding is transferred into the isotropic space through a message passing neural network (MPNN). Combined with structure encoding, semantic encoding can alleviate the anisotropy character of BERT. In addition, we propose the Negative Siamese Network (NSN) to analyze hard negative examples. The NSN focuses on widening the distance between hard negative samples and positive samples. After fine-tuning the NSN model, we conduct two tasks on four standard datasets. Experiment results show that our model achieves better performance than state-of-the-art models in capturing the subtle differences between semantically similar sentences.
Blabbeur - An Accessible Text Generation Authoring System for Unity

We present Blabbeur, a generative, context-aware, text generation system for Unity. It provides a simple, accessible context-free grammar inspired syntax allowing conditional generation and the surfacing of variables. Content requests are easily invoked in Unity scripts with relevant variables passed either as property dictionaries or through class interfaces. A persistent testing environment allows authors to quickly test their grammars against different contexts.
Research on the Application of Blockchain in Educational Resources

In recent years, the development of digital educational resources has advanced by leaps and bounds, but the problem of copyright protection of educational resources has become increasingly prominent. Based on the phenomenon that it is difficult to solve the problems of copyright traceability, platform monopoly and transaction protection by existing digital resource protection methods, using the characteristics that it is difficult to tamper with the blockchain achieves the purpose of copyright traceability, using the decentralization feature realizes the resource antitrust function, and using the smart contract creates a secure trading environment. It is based on Spring Boot framework, Fabric distributed ledger, asymmetric encryption technology, IPFS and other technologies putting forward the design scheme of education platform based on blockchain technology. From the technical level, let the creators of educational resources be willing to share, dare to share educational resources, and let more learners obtain the knowledge they want.
Participatory Design and Process Testing to Optimize Utility, Usability, and Acceptability of a Mobile Game for Promoting Evidence-Driven Public Health Decision-Making in Resource-Constrained Settings

Innovative game-based training methods that leverage the ubiquity of cellphones and familiarity with phone-based interfaces have the potential to transform the training of public health practitioners in low-income countries such as Liberia. This article describes the design, development, and testing of a prototype of the Figure It Out mobile game. The prototype game uses a disease outbreak scenario to promote evidence-based decision-making in determining the causative agent and prescribing intervention measures to minimize epidemiological and logistical burdens in resource-limited settings. An initial prototype of the game developed by the US team was playtested and evaluated by focus groups with 20 University of Liberia Masters of Public Health (UL MPH) students. Results demonstrate that the learning objectives-improving search skills for identifying scientific evidence and considering evidence before decision-making during a public health emergency-were considered relevant and important in a setting that has repeatedly and recently experienced severe threats to public health. However, some of the game mechanics that were thought to enhance engagement such as trial-and-error and choose-your-own-path gameplay, were perceived by the target audience as distracting or too time-consuming, particularly in the context of a realistic emergency scenario. Gameplay metrics that mimicked real-world situations around lives lost, money spent, and time constraints during public health outbreaks were identified as relatable and necessary considerations. Our findings reflect cultural differences between the game development team and end users that have emphasized the need for end users to have an integral part of the design team; this formative evaluation has critically informed next steps in the iterative development process. Our multidisciplinary, cross-cultural and cross-national design team will be guided by Liberia-based public health students and faculty, as well as community members who represent our end user population in terms of experience and needs. These stakeholders will make key decisions regarding game objectives and mechanics, to be vetted and implemented by game design experts, epidemiologists, and software developers.
Three-Tier Password Security Algorithm for Online Databases

Password security is a significant issue for any authenticating process, and different researchers in the past have proposed techniques such as hashing, salting, honeywords to make the process most secured. We provide a self-guaranteed secure program and its access methods to mitigate the risk of illegal attacks like DDoS. This method ensures the security of a program by giving method level security that bolsters the further implemented security measures. A step-up approach is implementing the access security measures that protect against SQL injection and script attacks. Lucubration of various papers suggests that most systems are compromised at the storage of login credentials. Our solution ensures the security of such measures by obscurity. By using multiple layers of additional security procedures before storing the data decreases the risk of break-in exponentially. Our attempt to securely store passwords includes salting the passwords, encrypting them, and finally hashing them so that no patterns are visible. This paper presents a new method that involves SQLi prevention, encrypting, salting and then hashing the password. The SALT is generated dynamically using two parameters, one of which is unique, and the Salt is not stored in the database.
Tagging terms in text

Abstract As with many tasks in natural language processing, automatic term extraction (ATE) is increasingly approached as a machine learning problem. So far, most machine learning approaches to ATE broadly follow the traditional hybrid methodology, by first extracting a list of unique candidate terms, and classifying these candidates based on the predicted probability that they are valid terms. However, with the rise of neural networks and word embeddings, the next development in ATE might be towards sequential approaches, i.e., classifying each occurrence of each token within its original context. To test the validity of such approaches for ATE, two sequential methodologies were developed, evaluated, and compared: one feature-based conditional random fields classifier and one embedding-based recurrent neural network. An additional comparison was added with a machine learning interpretation of the traditional approach. All systems were trained and evaluated on identical data in multiple languages and domains to identify their respective strengths and weaknesses. The sequential methodologies were proven to be valid approaches to ATE, and the neural network even outperformed the more traditional approach. Interestingly, a combination of multiple approaches can outperform all of them separately, showing new ways to push the state-of-the-art in ATE.
ORSuite

Reinforcement learning (RL) has received widespread attention across multiple communities, but the experiments have focused primarily on large-scale game playing and robotics tasks. In this paper we introduce ORSuite, an open-source library containing environments, algorithms, and instrumentation for operational problems. Our package is designed to motivate researchers in the reinforcement learning community to develop and evaluate algorithms on operational tasks, and to consider the true multi-objective nature of these problems by considering metrics beyond cumulative reward.
Bidirectional Distillation for Multi-Guidance Medical Dialogue Generation

Although researches on the dialogue system with deep learning methods have achieved good performance, medical dialogue generation confronts particular difficulties against other domains. As requiring highly accurate replies, many different types of external guidance signals are provided in previous studies to control the output and increase faithfulness. However, how these strategies compare and combine to each other is not known. In light of these challenges, we propose a multi-guidance model with bidirectional distillation for medical dialogue generation. Firstly, we fuse different guidance signals (keywords, categories and summaries) with neural sequence-to-sequence (Seq2Seq) model as teacher models. Meanwhile, we consider a simplified model without guidance as the student model. We also propose an attention mechanism to ensemble for the fusion of knowledge from multiple teachers. We further develop a bidirectional distillation module to exchange the knowledge between the teachers and a student from both sides during the training process. Through extensive experiments on medical dataset, we demonstrate the superiority of our proposed approach over state-of-the-art ones.
A multiobjective based Encryption scheme for content-based addressing in Blockchain

In today's world, due to the advanced technology development, the users store their information in an online platform called cloud. There are different types of storage mechanisms like Rados (object oriented), Cassandra (key oriented) and Interplanetary file storage system (IPFS) used in the cloud. Among these, the IPFS is the best storage mechanism because it creates the address based on the content. But individually IPFS suffers from the replicate problem. Hence, it combined with the Block chain technology. Even though it combined with the block chain technology it suffered from the data privacy and replicated data. In the existing system, the Cuckoo search optimization finds the optimal key and the similarity distance metric used for the analysis. Due to the selected distance metric, there is replicated data in the system. Hence, in this paper, a multi-objective optimization is proposed for finding the optimal key and distance metric. In this, the forest optimization determined the key based on the input data for Advanced standard encryption. Then, it also determined the squared Euclidean distance is the best metric for reducing the upload time and saving the storage space on the server. The proposed approach reduced its upload speed to 10.9% and 11.8% as compared to the existing approach.
Edge Attention Network for Image Deblurring and Super-Resolution

While deep learning-based single image super-resolution has progressed significantly, super-resolution of images containing blurring artifacts is still challenging. This paper proposes a unified model that can simultaneously perform deblurring and super-resolution for a given image, which is called Edge Attention Network (EAN). Our model employs an attention mechanism using the edge information in order to enhance the sharpness of the super-resolved output image. Experimental results demonstrate that our method outperforms existing super-resolution methods and separate application of deblurring and super-resolution.
Improve the Energy Efficiency of Datacenters with the Awareness of Workload Variability

In modern datacenters, huge energy consumption is a significant problem that remains to be solved. Previous works reduce the system energy consumption by switching the idle servers to a low-power state. However, the workload demands on servers change dynamically and mainly depend on the real-time workload status. To maintain the system energy efficiency, when providing servers with some servers reserved, the status of workloads should be carefully considered. Generally, the status of workloads is characterized by some key factors sampled from the workloads. However, under dynamic workload demands, the accuracy of these sampled values varies. To accurately assess the status of server workloads, in this paper, we propose a Dynamic Time Scale based server Provision (DTSP) method, which takes the variability of workloads into consideration when providing servers for workload demands. To obtain accurate factor values indicating real-time workload, DTSP samples several key workload factors, including the coefficient of variation of arrival intervals, the request arrival rates of current workload and previous workload, and the mean service time of current requests, with a dynamic compatible rate. With these sampled factors, DTSP can accurately estimate the demands of workloads on servers and provide appropriate numbers of servers for the dynamic workloads. Extensive experiments demonstrate that taking the workload variability into consideration, DTSP can significantly promote the energy efficiency of a datacenter.