Reviewerly/exHarmony huggingface dataset:
An Enhanced Feature Acquisition for Sentiment Analysis of English and Hausa Tweets

Due to the continuous and rapid growth of social media, opinionated contents are actively created by users in different languages about various products, services, events, and political parties.The automated classification of these contents prompted the need for multilingual sentiment analysis researches.However, the majority of research efforts are devoted to English and Arabic, English and German, English and French languages, while a great share of information is available in other languages such as Hausa.This paper proposes multilingual sentiment analysis of English and Hausa tweets using an Enhanced Feature Acquisition Method (EFAM).The method uses machine learning approach to integrate two newly defined Hausa features (Hausa Lexical Feature and Hausa Sentiment Intensifiers) and English feature to measure classification performance and to synthesize a more accurate sentiment classification procedure.The approach has been evaluated using several experiments with different classifiers in both monolingual and multilingual datasets.The experimental results reveal the effectiveness of the approach in enhancing feature integration for multilingual sentiment analysis.Similarly, by using features drawn from multiple languages, we can construct machine learning classifiers with an average precision of over 65%.
MPDP <i>k</i>-medoids: Multiple partition differential privacy preserving <i>k</i>-medoids clustering for data publishing in the Internet of Medical Things

The tremendous growth of Internet of Medical Things has led to a surge in medical user data, and medical data publishing can provide users with numerous services. However, neglectfully publishing the data may lead to severe leakage of user’s privacy. In this article, we investigate the problem of data publishing in Internet of Medical Things with privacy preservation. We present a novel system model for Internet of Medical Things user data publishing which adopts the proposed multiple partition differential privacy k-medoids clustering algorithm for data clustering analysis to ensure the security of user data. Particularly, we propose a multiple partition differential privacy k-medoids clustering algorithm based on differential privacy in data publishing. Based on the traditional k-medoids clustering, multiple partition differential privacy k-medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user’s privacy information. Comprehensive analysis and simulations demonstrate that our method can not only meet the requirements of differential privacy but also retain the better availability of data clustering.
Classification of traffic over collaborative IoT and Cloud platforms using deep learning recurrent LSTM

Internet of Things (IoT) and cloud based collaborative platforms are emerging as new infrastructures during recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT-cloud based collaborative platforms to utilize the channel capacity optimally for transmitting the benign traffic and to block the malicious traffic. The traffic classification mechanism should be dynamic and capable enough to classify the network traffic in a quick manner, so that the malevolent traffic can be identified in earlier stages and benign traffic can be channelized to the destined nodes speedily. In this paper, we are presenting deep learning recurrent LSTM based technique to classify the traffic over IoT-cloud platforms. Machine learning techniques (MLTs) have also been employed for comparison of the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is classified into three classes namely Tor-Normal, NonTor-Normal and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet classify the traffic accurately and also helps in reducing the network latency and in enhancing the data transmission rate as well as network throughput.
Ranked Deep Web Page Detection Using Reinforcement Learning and Query Optimization

This paper proposes a novel algorithm based on reinforcement learning-entitled asynchronous advantage actor-critic (A3C). Overflow queries are optimized to crawl the ranked deep web. A3C assigns the reward and penalty to the various queries. Queries are derived from the domain-based taxonomy that helps to fill the search forms. Overflow queries are the collection of queries that match with more than k number of results and only top k matched results are retrieved. Low ranked documents beyond k results are not accessible and lead to low coverage. Overflow queries are optimized to convert into non-overflow queries based on the proposed technique and lead to more coverage. As of yet, no research work has been explored by using A3C with taxonomy in the domain of ranked deep web. The experimental results show that the proposed technique outperforms the three other techniques (i.e., document frequency, random query, and high frequency) in terms of average improvement metric by 26%, 69%, and 92%, respectively.
Explaining Deep Reinforcement Learning Agents in the Atari Domain through a Surrogate Model

One major barrier to applications of deep Reinforcement Learning (RL) both inside and outside of games is the lack of explainability. In this paper, we describe a lightweight and effective method to derive explanations for deep RL agents, which we evaluate in the Atari domain. Our method relies on a transformation of the pixel-based input of the RL agent to a symbolic, interpretable input representation. We then train a surrogate model, which is itself interpretable, to replicate the behavior of the target, deep RL agent. Our experiments demonstrate that we can learn an effective surrogate that accurately approximates the underlying decision making of a target agent on a suite of Atari games.
Concept Drift Segmentation via Kolmogorov-Trees

The notion of concept drift refers to the phenomenon that the data distribution changes over time.If drift occurs, machine learning models need adjustment.Since drift can be inhomogeneous, suitable actions depending on the location in data space.In this paper we address the challenge to partition the data space into segments with homogeneous drift characteristics.We formalize this objective as an independence criterion, and derive a robust and efficient training algorithm based thereon.We evaluate the efficiency of the method in comparison to existing technologies: the identification of drifting clusters, and the estimation of a conditional density distribution.
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators

Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks.We present Multi-Stage Prompting, a simple and automatic approach for leveraging pre-trained language models to translation tasks.To better mitigate the discrepancy between pre-training and translation, MSP divides the translation process via pre-trained language models into multiple separate stages: the encoding stage, the re-encoding stage, and the decoding stage.During each stage, we independently apply different continuous prompts for allowing pretrained language models better shift to translation tasks.We conduct extensive experiments on three translation tasks.Experiments show that our method can significantly improve the translation performance of pre-trained language models.
Towards an Agenda for Open Language Archiving

The Open Language Archives Community (OLAC) provides a comprehensive infrastructure that has allowed our community to index and discover language resources over the past 20 years. However, OLAC infrastructure has fallen behind as the digital libraries community has continued to evolve. New investment is required in order to move OLAC into the digital libraries mainstream. This paper reports on the first 20 years of OLAC and on an agenda leading to a more sustainable future for open language archiving.
Large Scale Substitution-based Word Sense Induction

We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora.The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where each sense is associated with indicative words.Evaluation on English Wikipedia that was sense-tagged using our method shows that both the induced senses, and the per-instance sense assignment, are of high quality even compared to WSD methods, such as Babelfy.Furthermore, by training a static word embeddings algorithm on the sense-tagged corpus, we obtain high-quality static senseful embeddings.These outperform existing senseful embeddings methods on the WiC dataset and on a new outlier detection dataset we developed.The data driven nature of the algorithm allows to induce corpora-specific senses, which may not appear in standard sense inventories, as we demonstrate using a case study on the scientific domain.
Two-photon scattering and correlation in a four-terminal waveguide system

Scattering and correlation properties of a two-photon (TP) pulse are studied in a four-terminal waveguide system, i.e., two one-dimensional waveguides connected by a Jaynes-Cummings emitter (JCE). The wave function approach is utilized to exactly calculate the real-time dynamic evolution of the TP transport. When the width of the incident TP Gaussian pulse is much larger than the photon wavelength, the TP transmission spectra approach that of the corresponding single photon cases and are almost independent of the pulse width. On the contrary, as the pulse width is comparable to the photon wavelength, the TP transmission and correlation both show strong dependence on the pulse width. The resonant scattering due to the JCE and the photon interference together determine the TP correlation. When the distance between the TPs is small, the TP correlations between any two terminals for the scattered TP pulse are much different from those for the incident TP pulse and therefore, such a four-terminal waveguide system provides a way to control the TP correlation.
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog

Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD).These approaches, however, exploit general dialogic corpora (e.g., Reddit) and thus presumably fail to reliably embed domain-specific knowledge useful for concrete downstream TOD domains.In this work, we investigate the effects of domain specialization of pretrained language models (PLMs) for TOD.Within our DS-TOD framework, we first automatically extract salient domain-specific terms, and then use them to construct DOMAINCC and DO-MAINREDDIT -resources that we leverage for domain-specific pretraining, based on (i) masked language modeling (MLM) and (ii) response selection (RS) objectives, respectively.We further propose a resource-efficient and modular domain specialization by means of domain adapters -additional parameter-light layers in which we encode the domain knowledge.Our experiments with prominent TOD tasksdialog state tracking (DST) and response retrieval (RR) -encompassing five domains from the MULTIWOZ benchmark demonstrate the effectiveness of DS-TOD.Moreover, we show that the light-weight adapter-based specialization (1) performs comparably to full fine-tuning in single domain setups and (2) is particularly suitable for multi-domain specialization, where besides advantageous computational footprint, it can offer better TOD performance.
Quantum trajectory theory and simulations of nonlinear spectra and multiphoton effects in waveguide-QED systems with a time-delayed coherent feedback

We study the nonlinear spectra and multi-photon correlation functions for the waveguide output of a two-level system (including realistic dissipation channels) with a time-delayed coherent feedback. We compute these observables by extending a recent quantum trajectory discretized-waveguide (QTDW) approach which exploits quantum trajectory simulations and a collisional model for the waveguide to tractably simulate the dynamics. Following a description of the general technique, we show how to calculate the first and second order quantum correlation functions, in the presence of a coherent pumping field. With a short delay time, we show how feedback can be used to filter out the central peak of the Mollow triplet or switch the output between bunched and anti-bunched photons by proper choice of round trip phase. We further show how the loop length and round trip phase effects the zero-time second order quantum correlation function, an indicator of bunching or anti-bunching. New resonances introduced through the feedback loop are also shown through their appearance in the incoherent output spectrum from the waveguide. We explain these results in the context of the waiting time distributions of the system output and individual trajectories, uniquely stochastic observables that are easily accessible with the QTDW model.
Unfounded Sets for Disjunctive Hybrid MKNF Knowledge Bases

Combining the closed-world reasoning of answer set programming (ASP) with the open-world reasoning of ontologies broadens the space of applications of reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in some cases without increasing the complexity of reasoning tasks. However, in many cases, solver development is lagging behind. As the result, the only known method of solving disjunctive hybrid MKNF knowledge bases is based on guess-and-verify, as formulated by Motik and Rosati in their original work. A main obstacle is understanding how constraint propagation may be performed by a solver, which, in the context of ASP, centers around the computation of \textit{unfounded atoms}, the atoms that are false given a partial interpretation. In this work, we build towards improving solvers for hybrid MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded sets for these knowledge bases, identify lower complexity bounds, and demonstrate how we might integrate these developments into a DPLL-based solver. We discuss challenges introduced by ontologies that are not present in the development of solvers for disjunctive logic programs, which warrant some deviations from traditional definitions of unfounded sets. We compare our work with prior definitions of unfounded sets.
Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing

We present substructure distribution projection (SUBDP), a technique that projects a distribution over structures in one domain to another, by projecting substructure distributions separately.Models for the target domain can then be trained, using the projected distributions as soft silver labels.We evaluate SUBDP on zeroshot cross-lingual dependency parsing, taking dependency arcs as substructures: we project the predicted dependency arc distributions in the source language(s) to target language(s), and train a target language parser on the resulting distributions.Given an English treebank as the only source of human supervision, SUBDP achieves better unlabeled attachment score than all prior work on the Universal Dependencies v2.2 (Nivre et al., 2020) test set across eight diverse target languages, as well as the best labeled attachment score on six languages.In addition, SUBDP improves zeroshot cross-lingual dependency parsing with very few (e.g., 50) supervised bitext pairs, across a broader range of target languages.
Requirements Analysis in Disruptive Engineering Solutions Using the Paradigm of Living Systems

A particular characteristic of disruptive products is in reengineering advanced technologies for addressing the needs of low-end consumers and/or non-consumers, to transform them into new consumers. This requires a lean co-creative analysis of requirements with all stakeholders involved. Even if a theory encourages the continuous connection of designers and users throughout the design lifecycle for agile adaptation of requirements to the new experiences of users by intersecting them with various versions of the prototype, the rigid budget and time allocated to the design project require novel approaches to clarify the right vectors of product-evolution from the very early design stages of the project lifecycle—allowing agile approaches to fine-tune the set of requirements. In this context, an analysis process of requirements that uses a constructor inspired by living systems is introduced in this paper. This constructor identifies gaps in requirement formulation and indicates areas where improvements must be undertaken. The method is applied in the case of a new cybersecurity software solution that targets micro and small companies.
Active and Passive Collection of SSH Key Material for Cyber Threat Intelligence

This article describes a system for storing historical forensic artifacts collected from SSH connections. This system exposes a REST API in a similar fashion as passive DNS databases, malware hash registries, and SSL notaries with the goal of supporting incident investigations and monitoring of infrastructure.
Examining Compliance with Personal Data Protection Regulations in Interorganizational Data Analysis

The development of big data analysis technologies has changed how organizations work. Tech giants, such as Google and Facebook, are well positioned because they possess not only big data sets but also the in-house capability to analyze them. For small and medium-sized enterprises (SMEs), which have limited resources, capacity, and a relatively small collection of data, the ability to conduct data analysis collaboratively is key. Personal data protection regulations have become stricter due to incidents of private data being leaked, making it more difficult for SMEs to perform interorganizational data analysis. This problem can be resolved by anonymizing the data such that reidentifying an individual is no longer a concern or by deploying technical procedures that enable interorganizational data analysis without the exchange of actual data, such as data deidentification, data synthesis, and federated learning. Herein, we compared the technical options and their compliance with personal data protection regulations from several countries and regions. Using the EU’s GDPR (General Data Protection Regulation) as the main point of reference, technical studies, legislative studies, related regulations, and government-sponsored reports from various countries and regions were also reviewed. Alignment of the technical description with the government regulations and guidelines revealed that the solutions are compliant with the personal data protection regulations. Current regulations require “reasonable” privacy preservation efforts from data controllers; potential attackers are not assumed to be experts with knowledge of the target data set. This means that relevant requirements can be fulfilled without considerably sacrificing data utility. However, the potential existence of an extremely knowledgeable adversary when the stakes of data leakage are high still needs to be considered carefully.
Discrete Lion Swarm Optimization Algorithm for face recognition

In various applications, face recognition plays an important role such as identification of a person, biometrics via their Closed-Circuit Television (CCTV) cameras, identity cards and etc. Besides, various biometrics like palm print, fingerprint, iris, etc plays a significant role.Therefore, in this research, a face recognition technique is developed for the classification phase as well as feature extraction.Moreover, Discrete Lion Swarm Optimization Algorithm (DLSA) is developed Deep Belief Network (DBN) for face recognition.At first, in the database, the images experience feature extraction here the feature like m-Co-HOG, Kernel based Scale Invariant Feature Transform (K-SIFT) , besides with Active Appearance Models (AAM) features which are extracted from the image.Subsequently, developed DLSA-DBN is used for the classification.The simulation of the developed technique is performed by exploiting the CVL database.In addition, the developed model outperforms the conventional models for False Acceptance Rate (FAR), accuracy, and False Rejection Rate (FRR), correspondingly.
Approximation Fixpoint Theory for Non-Deterministic Operators and Its Application in Disjunctive Logic Programming

Approximation fixpoint theory (AFT) constitutes an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonotonic reasoning, such as logic programming, default logic and autoepistemic logic. In this paper, we extend AFT to non-deterministic constructs such as disjunctive information. This is done by generalizing the main constructions and corresponding results to non-deterministic operators, whose ranges are sets of elements rather than single elements. The applicability and usefulness of this generalization is illustrated in the context of disjunctive logic programming.
Smooth-RRT*: Asymptotically Optimal Motion Planning for Mobile Robots under Kinodynamic Constraints

Nowadays, various algorithms based on the Rapidly-exploring Random Tree (RRT) methods are utilized to solve motion planning problems. Based on the RRT*, we developed a novel reconnection method that enables the planner to directly generate a smooth curved trajectory. Meanwhile, kinodynamic constraints of the robots are considered to generate the control input, which improves the feasibility of the algorithm. The trajectory planned by the Smooth-RRT* is significantly suitable for the non-holonomic robots. Planning tests are conducted in four scenarios to demonstrate performance of the proposed algorithm in comparison with the original RRT* and kinodynamic-RRT (Kino-RRT). Smooth-RRT* yields shorter and smoother planned path in all the scenarios compared with the Kino-RRT. It finds a solution with fewer expansion nodes than the RRT* under the same time consumption. The results demonstrate that the proposed algorithm can generate a smooth trajectory satisfied with the kinodynamic constraints and ensure the asymptotic optimality.
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.Typically, the wrongly labeled instances comprise numbers of incomplete and inaccurate annotation noise, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the whole training set.To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutuallybeneficial manner to iteratively perform noisy label refinery.Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising.Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods 1 .