Reviewerly/exHarmony huggingface dataset:
IoT Hacking: Cyber Security Point of View

With the introduction of Internet of things (IoT) into our lives, it has been simplified to a greater extent. Being able to communicate over the network without any direct communication with human and computer or human and human, On the other hand, threats to the network of Internet of Things has also increased. Certain exploits and vulnerabilities have emerged since the development of Internet of Things. The most common vulnerability arises due to the firmware version of the IoT device. It is difficult to identify as well as update the device firmware version. Due to this incapability, it becomes easier to hack an IoT device. This paper identifies the current issues and provide with the latest solutions to secure IoT devices against attackers constantly trying to penetrate the network.
Knowledge Refactoring for Inductive Program Synthesis

Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the knowledge refactoring problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. A key feature of Knorf is that, rather than simply removing knowledge, it also introduces new knowledge through predicate invention. We evaluate our approach on two domains: building Lego structures and real-world string transformations. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.
Jointly Identifying Rhetoric and Implicit Emotions via Multi-Task Learning

Rhetorical implicit emotion identification is one of important and challenging tasks in natural language processing.We observe that each rhetoric may express certain evidence of semantic and syntactic patterns.Then, we design a gate mechanism based classification module to capture respective rhetorical representation and identify each rhetoric.Moreover, sentences carved with rhetoric tends to express emotions in subtle ways.We thus propose a new multi-task learning framework that can encode the categorical correlation between tasks to improve the performance of rhetoric and emotion identification problem.Experimental results validate the benefit of the proposed model over state-of-the-art baselines for rhetoric and emotion identification tasks.In addition, a new Chinese rhetorical implicit emotion dataset was constructed and will be released in this work.
Learning Invariant Representations using Inverse Contrastive Loss

Learning invariant representations is a critical first step in a number of machine learning tasks. A common approach corresponds to the so-called information bottleneck principle in which an application dependent function of mutual information is carefully chosen and optimized. Unfortunately, in practice, these functions are not suitable for optimization purposes since these losses are agnostic of the metric structure of the parameters of the model. We introduce a class of losses for learning representations that are invariant to some extraneous variable of interest by inverting the class of contrastive losses, i.e., inverse contrastive loss (ICL). We show that if the extraneous variable is binary, then optimizing ICL is equivalent to optimizing a regularized MMD divergence. More generally, we also show that if we are provided a metric on the sample space, our formulation of ICL can be decomposed into a sum of convex functions of the given distance metric. Our experimental results indicate that models obtained by optimizing ICL achieve significantly better invariance to the extraneous variable for a fixed desired level of accuracy. In a variety of experimental settings, we show applicability of ICL for learning invariant representations for both continuous and discrete extraneous variables. The project page with code is available at https://github.com/adityakumarakash/ICL.
Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract)

In this paper, we address the problem of extracting causal knowledge from text documents in a weakly supervised manner. We target use cases in decision support and risk management, where causes and effects are general phrases without any constraints. We present a method called CaKNowLI which only takes as input the text corpus and extracts a high-quality collection of cause-effect pairs in an automated way. We approach this problem using state-of-the-art natural language understanding techniques based on pre-trained neural models for Natural Language Inference (NLI). Finally, we evaluate the proposed method on existing and new benchmark data sets.
Augmented Reality Berbasis Android Sebagai Media Pembelajaran Sistem Tata Surya

Along with the development, the application can be used as a medium for learning. Augmented Reality is a technology that combines two-dimensional’s virtual objects and three-dimensional’s virtual objects into a real three-dimensional’s then projecting the virtual objects in real time and simultaneously. The introduction of Solar System’s material, students are invited to get to know the planets which are directly encourage students to imagine circumtances in the Solar System. Explenational of planets form and how the planets make the revolution and rotation in books are considered less material’s explanation because its only display objects in 2D. In addition, students can not practice directly in preparing the layout of the planets in the Solar System. By applying Augmented Reality Technology, information’s learning delivery can be clarified, because in these applications are combined the real world and the virtual world. Not only display the material, the application also display images of planets in 3D animation’s objects with audio.
A Survey on Securing IoT Ecosystems and Adaptive Network Vision

The rapid growth of Internet-of-Things (IoT) devices and the large network of interconnected devices pose new security challenges and privacy threats that would put those devices at high risk and cause harm to the affiliated users.This paper emphasizes such potential security challenges and proposes possible solutions in the field of IoT Security, mostly focusing on automated or adaptive networks.Considering the fact that IoT became widely adopted, the intricacies in the security field tend to grow expeditiously.Therefore, it is necessary for businesses to adopt new security protocols and to the notion of automated network security practices driven by analytic and intelligence, to ensure a prompt response to attacks there by protecting the privacy and data integrity of users.The main prospect of this paper is to highlight some extensive reviews on standardizing security solutions by means of adaptive networks, a programmable environment that is driven by analytical and intelligence which expands on the autonomous networking concepts and transforms static networks into a dynamic environment.Furthermore, this paper also inspects some of the Machine Learning techniques that can be used to enhance security and compares different techniques to find the best fit to IoT.
Geomodels of space monitoring of water bodies

Theme of the paper is the solution of flooding geomodels creation for Dniester river basin territory, which uses contact and remote measurements. Natural and anthropogenic factors causing groundwater level rising were analyzed for localization of flooded zones and forecasting of geometric characteristics. Geographic information systems for researched area include the spatial binding of the hydrological elements and observation points, digital model of relief preparation, basin allocation catchment, flooded areas modeling. Structural features include the size and shape of object, brightness disposal within the object, image texture, some others. Possibility of available remote sensing data use allows reducing temporal and economic cost for conducting additional ground measurements for possible flooded areas determining. These methods of geomodels creation are realized for the territory of Dniester Canyon, the hydrogeologic feature of which is connected to the Upper Cretaceous horizon and groundwater.
A multidimensional model of interaction as a framework for a phenomenon-driven approach to communication

Interaction between people is a cornerstone of being human. Despite huge developments in languages and communicative skills, interaction often fails, which causes problems and costs in everyday life and work. An inability to conduct dialogue also produces conflicts between groups of people, states and religions. Therefore, there are good reasons to claim that miscommunication and failures in interaction are among the most serious problems in the world. Researchers from different fields - linguistics, sociology, anthropology, psychology, brain research, philosophy - have tried to tackle this complex phenomenon. Their method-driven approaches enrich our understanding of the features of interaction in many ways. However, what is lacking is an understanding of the very essence of interaction, which needs a more holistic, phenomenon-driven approach. The aim of this paper is to show that the only way to reach this goal is multidisciplinarity, that is, using the results and methods of different fields of research. This is not an easy goal and task because the way of thinking and doing research varies greatly discipline-wise. A further obstacle is the researchers training, which, as a rule, focuses on the tradition of only one field of research. The Multidimensional Model of Interaction provides a good framework for a more holistic approach to interaction by viewing the complex phenomenon from different angles. The model includes various phases of the process of interaction, beginning with the choice of the topic by the speaker and ending with identification of the reference by the recipient, as well as the mental worlds of the interlocutors (knowledge, attitudes, values, emotional state etc.), recipient design (accommodation of speech) and external circumstances.
Deep Recurrent Belief Propagation Network for POMDPs

In many real-world sequential decision-making tasks, especially in continuous control like robotic control, it is rare that the observations are perfect, that is, the sensory data could be incomplete, noisy or even dynamically polluted due to the unexpected malfunctions or intrinsic low quality of the sensors. Previous methods handle these issues in the framework of POMDPs and are either deterministic by feature memorization or stochastic by belief inference. In this paper, we present a new method that lies somewhere in the middle of the spectrum of research methodology identified above and combines the strength of both approaches. In particular, the proposed method, named Deep Recurrent Belief Propagation Network (DRBPN), takes a hybrid style belief updating procedure − an RNN-type feature extraction step followed by an analytical belief inference, significantly reducing the computational cost while faithfully capturing the complex dynamics and maintaining the necessary uncertainty for generalization. The effectiveness of the proposed method is verified on a collection of benchmark tasks, showing that our approach outperforms several state-of-the-art methods under various challenging scenarios.
Mobile Money and the Impact of Mobile Phone Regulatory Enforcement Among the Urban Poor in Tanzania

Mobile money provides a tool for survival, particularly in urban conditions shaped by city regulations that make microvending difficult for the poor. An analysis of 165 interviews conducted in two low-income neighborhoods in Dar es Salaam, Tanzania over 8 years demonstrates how interlocked layers of technology and interaction make mobile money services semiformal. I introduce two mobile money-enabled survival strategies: intrahousehold transfers for day-to-day survival (transfers within the same city) and resource safeguarding through kin remittances of start-up capital (home-based subsistence business capital stored for kin access in emergencies). The recent tightening of mobile phone regulations in the global South has disrupted users’ multilevel and formal/informal-hybrid infrastructures of money movement in these communities. Such tougher regulations could result in a new digital divide that hinders rather than facilitates the financial inclusion of the poor.
Identifying Similar Question Pairs Using Machine Learning Techniques

Background/Objectives: Every day millions of people visit search engines like Quora, reedit, stack overflow, etc., the demand for new intelligent techniques is growing, to help individuals find better solutions.Methods: In our proposed system, the Quora datasets were filtered using SQLite which takes one-quarter of the time taken to pre-process the same dataset using existing approaches like python functions.We used machine learning techniques namely the Random Forest, Logistic Regression, Linear SVM (Support Vector Machine) and XGBoost to analyze and identify the most suitable model.Findings: The error log loss functions (0.887, 0.521, 0.654 and 0.357) of the above machine learning techniques were analyzed and compared respectively.The performance of XGBoost is the best among the other models, hence XGBoost is the most efficient model.Conclusion/Future Scope: It is concluded that XGBoost has outperformed other machine learning techniques discussed in the study.It is also found that pre-processing using SQLite has improved the response time.In the future, we would like to apply a similar approach for various other search engines that are available like reedit, stack overflow, etc. and one could ensemble the best models of each type (linear, tree-based, and neural network).
Visualizing Vehicle Acceleration and Braking Energy at Intersections along a Major Traffic Corridor

Automobiles approaching a controlled intersection need to brake and come to a full stop when the signal transitions from green to red, and the vehicle must later accelerate to normal speeds after the signal changes back to green. These stops and starts associated with normal signal changes lead to unnecessary energy consumption and vehicle emissions. Previous studies have revealed that the optimization of traffic intersections' signal controls and coordination facilitates smoother traffic flows with reduced stop-and-go driving, which can significantly reduce traffic congestion and unnecessary fuel waste. This paper presents an interactive visual analytics dashboard that allows transportation planners to explore and analyze energy consumption patterns resulting from temporally varying traffic signal phases at multiple intersections along a major transportation corridor using traffic simulation outputs. The visual dashboard is implemented as an accessible and responsive web application and employs a combination of visualization techniques to cover multiple aspects of vehicle acceleration and braking at multiple adjacent intersections along a corridor. The paper presents a case study of a simulated traffic scenario on the Shallowford Road traffic corridor located in Chattanooga, Tennessee to demonstrate the capability of the visual dashboard.
Hybrid metaheuristic approach for robot path planning in dynamic environment

Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.
NPDHunter: Efficient Null Pointer Dereference Vulnerability Detection in Binary

Null pointer dereference (NPD) is a widespread vulnerability that occurs whenever an executing program attempts to dereference a null pointer. NPD vulnerability can be exploited by hackers to maliciously crash a process to cause a denial of service or execute an arbitrary code under specific conditions. This typical taint-style vulnerability requires an accurate data dependency analysis to trace whether a source is propagated to a sensitive sink without proper sanitization. The primary challenge in data dependency analysis is pointer aliasing, which may significantly affect the vulnerability detection accuracy. Although there have been many studies and open-source tools, they still have limitations when detecting a real-world binary. In this paper, we propose a static binary analysis approach to detect an NPD vulnerability. To improve detection accuracy and practicality, we first identify two challenges that affect the accuracy of binary NPD detection: (i) pointer aliasing, and (ii) untrusted source identification. Then we implement a prototype of the proposed approach, NPDHunter, and evaluate it against 318 test cases provided by Juliet Test Suite v1.3. For the Juliet dataset, NPDHunter is accurate in detecting NPDs and generates 0% false negatives; as compared to bap-toolkit and cwe_checker, which have false-negative rates of 70.89% and 89.81%, respectively. We also evaluate NPDHunter for real-world binaries which recently reported NPD vulnerability. We have analyzed XNU kernel (large-scale), Redis, Bitlbee, libredwg, and libvncserver binaries and NPDHunter can detect all NPD cases, which justifies its usefulness for real-world binaries; compiled for x86_64 architecture.
Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment

In active learning, the ignorance of aligning unlabeled samples' distribution with that of labeled samples hinders the model trained upon labeled samples from selecting informative unlabeled samples. In this paper, we propose an agreement-discrepancy-selection (ADS) approach, and target at unifying distribution alignment with sample selection by introducing adversarial classifiers to the convolutional neural network (CNN). Minimizing classifiers' prediction discrepancy (maximizing prediction agreement) drives learning CNN features to reduce the distribution bias of labeled and unlabeled samples, while maximizing classifiers' discrepancy highlights informative samples. Iterative optimization of agreement and discrepancy loss calibrated with an entropy function drives aligning sample distributions in a progressive fashion for effective active learning. Experiments on image classification and object detection tasks demonstrate that ADS is task-agnostic, while significantly outperforms the previous methods when the labeled sets are small.
Boundary Objects in Design Studies: Reflections on the Collaborative Creation of Isochrone Maps

Abstract We propose to take an artifact‐centric approach to design studies by leveraging the concept of boundary object. Design studies typically focus on processes and articulate design decisions in a project‐specific context with a goal of transferability. We argue that design studies could benefit from paying attention to the material conditions in which teams collaborate to reach design outcomes. We report on a design study of isochrone maps following cartographic generalization principles. Focusing on boundary objects enables us to characterize five categories of artifacts and tools that facilitated collaboration between actors involved in the design process (structured collections, structuring artifacts, process‐centric artifacts, generative artifacts, and bridging artifacts). We found that artifacts such as layered maps and map collections played a unifying role for our inter‐disciplinary team. We discuss how such artifacts can be pivotal in the design process. Finally, we discuss how considering boundary objects could improve the transferability of design study results, and support reflection on inter‐disciplinary collaboration in the domain of Information Visualization.
Innovative Ideas for the Teaching of Guzheng in Colleges and Universities based on the New Curriculum Reform

As a traditional musical instrument, the guzheng holds a very important status in the Chinese traditional culture. In recent years, with the increase of opportunities for exchanges and cooperation between the West and China, guzheng is rapidly favored in the foreign music circle. As the main institute for cultivating high-level talents in the society, the academic discipline of music in various universities should actively promote the education reform of guzheng based on the new curriculum reform combined with innovations and modifications of the national instrumental music teaching model to efficiently teach guzheng.
Organization of higher education in context of digitalization: online learning experience at pandemic, development trends

It is highlighted in the study that digital transformations of the fourth industrial revolution are changing the modern human world, encouraging a person to acquire new competencies and become a qualified specialist in the digital economy. In connection with the progressive scientific and technical development, the institute of higher education is being transformed; new previously unknown approaches to the organization of higher education appear. One of such approaches is the introduction of online learning in higher education. The authors refer to the experience of 2020, when, during the pandemic of the new coronavirus infection, the whole world began to actively transfer work processes to a remote format, which also affected the institute of education. Universities around the world introduced distance learning using information technology, which helped students and faculty communicate at a distance. Based on the analysis, the following conclusions were drawn: digitalization of higher education in Russian universities is currently associated with the use of information and communication technologies in the educational environment and to a lesser extent with the use of electronic educational environment in the indirect interaction of students and teaching staff. In our opinion, it is necessary for the digitalization process to be consistent and not to be reduced to full distance learning, but to competently integrate information technology into the traditional learning process.
Lumiphys

This work proposes an energy physicalization for long-term sharing of Smart Campus data: Lumiphys. We designed Lumiphys to increase the community's awareness of the campus energy consumption --- inspired by previous examples of energy visualizations but based on criteria to ensure its durability, integration with the environment, and visual encoding familiarity. Our proposal contrasts with previous work, which focuses on residential power data and ephemeral installations. This paper contributes to the reflection on how to design long-term energy physicalizations and Smart Campus visualizations.