Reviewerly/exHarmony huggingface dataset:
Identifying Overly Restrictive Matching Patterns in SMT-Based Program Verifiers

Universal quantifiers occur frequently in proof obligations produced by program verifiers, for instance, to axiomatize uninterpreted functions and to express properties of arrays. SMT-based verifiers typically reason about them via E-matching, an SMT algorithm that requires syntactic matching patterns to guide the quantifier instantiations. Devising good matching patterns is challenging. In particular, overly restrictive patterns may lead to spurious verification errors if the quantifiers needed for a proof are not instantiated; they may also conceal unsoundness caused by inconsistent axiomatizations. In this paper, we present the first technique that identifies and helps the users remedy the effects of overly restrictive matching patterns. We designed a novel algorithm to synthesize missing triggering terms required to complete a proof. Tool developers can use this information to refine their matching patterns and prevent similar verification errors, or to fix a detected unsoundness.
Towards Understanding the Monetization and Censorship Aspect of Streaming Media

The high production and utilization of digital content have led to an increase in content censorship. YouTube censors its digital content by Demonetization of videos. Demonetization is a process in which content creators are denied paid ads in their YouTube videos. The creators are refused income. In cases, their income on the video-hosting platform is lessened, and their video is less likely to be promoted or recommended, eventually getting censored. There is little known about YouTube's censorship algorithm as it exists as a Blackbox to the world. The Work-in-Progress (WIP) proposes a methodology that employs four machine learners, i.e., C 4.5, Random Forest, Logistic Regression and Support Vector Machine, to predict if changes in the meta-data of the YouTube video will lead to (demonetization) censorship of the video. Our methodology requires little time to train and achieve an accuracy of up to 79%.
Semiglobal Stereo Matching Algorithm Based on Improved Census Transformation

Binocular stereo matching is in an effort to obtain disparity maps from binocular images taken by the same camera. Existing methods resulted in mismatches in the discontinuous regions. In this study, we put forward a innovative binocular stereo correspondence method based on an improved census transformation. After that, semiglobal stereo matching algorithm was performed on matching cost aggregation stage. Finally, we further conducted Left and Right Consistency Check and median filter to generate better disparities. The results indicated that compared with the original census transform, the disparities obtained by our method was continuous and dense, and the mismatch rate is lower.
Search-Based Planning and Reinforcement Learning for Autonomous Systems and Robotics

In this chapter, we address the competent Autonomous Vehicles should have the ability to analyze the structure and unstructured environments and then to localize itself relative to surrounding things, where GPS, RFID or other similar means cannot give enough information about the location. Reliable SLAM is the most basic prerequisite for any further artificial intelligent tasks of autonomous mobile robots. The goal of this paper is to simulate a SLAM process on advanced software development. The model represents the system itself, whereas the simulation represents the operation of the system over time. And the software architecture will help us to focus our work to realize our wish with least trivial work. It is an open-source meta-operating system, which provides us tremendous tools for robotics related problems. Specifically, we address the advanced vehicles should have the ability to analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.
Approximate Computing for Stream Analytics

Approximate computing has become a promising mechanism to trade off accuracy for efficiency. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing – based on the chosen sample size – can make a systematic trade-off between the output accuracy and computation efficiency. Unfortunately, the state-of-the-art systems for approximate computing primarily target batch analytics, where the input data remains unchanged during the course of computation. Thus, they are not well-suited for stream analytics. This motivated the design of StreamApprox– a stream analytics system for approximate computing. To realize this idea, an online stratified reservoir sampling algorithm is designed to produce approximate output with rigorous error bounds. Importantly, the proposed algorithm is generic and can be applied to two prominent types of stream processing systems: (1) batched stream processing such as Apache Spark Streaming, and (2) pipelined stream processing such as Apache Flink.
Interaction management in the sphere of differently directed blockchain platforms

Attempts to realize the opening possibilities for the practical use of new forms of communication at various levels are guaranteed to lead to a wide differentiation of the areas of interaction themselves. To date, it is blockchain technologies that have proven to be the most adapted to fully satisfy a number of the most important needs of society. The need to create and debug a unified information base, simultaneously linked to the information bases of various ministries, is clearly manifested. Promising approaches to solving intradepartmental and inter-departmental interactions should be determined already at the stage of developing technical specifications for those organizations that are responsible for scientific support and software for the created services. The most complete implementation of the options available today, which open up unique opportunities for using multidirectional blockchain platforms, ensuring the achievement of mutually enriching effects on the basis of this, is the most important issue that initially needs coordinated solutions.
Minimal-envy Conference Paper Assignment: Formulation and a Fast Iterative Algorithm

In conference review management, it is very common that many papers being assigned are not liked by some reviewers due to their specialties or personal interests. In this paper, we consider a reviewer might only envy other reviewers who get a better set of papers than his. In this context, we proposes a new bi-objective optimization assignment model to efficiently and fairly assign papers to many competing reviewers, in which an index that measures the amount of envy is introduced. A fast iterative algorithm is proposed to solve the problem. The experimental results on real datasets shows that the algorithm achieves efficient and fair results and good computing performance, especially on large scale problems. This work is also meaningful for a number of similar allocation or matching problems.
VEDA - moVE DAta to balance the grid: research directions and recommendations for exploiting data centers flexibility within the power system

This paper aims at discussing visions and research directions to investigate the value of data centers flexibility within sustainable electrical energy systems. While optimizing the energy consumption and task scheduling within data centers located in different time zones and connected at national and international level, it is possible to balance the local power grids, to allow a better penetration of intermittent renewable energy sources, and a more economical way to address peak demand by avoiding or postponing costly investments in network expansion. Challenges and opportunities that behind the exploitation of data centers flexibility within sustainable electrical energy systems will be discussed. An interdisciplinary approach to tackle these kind of problems will be proposed, and visions for a novel framework called VEDA (moVE DAta to balance the grid) will be outlined.
Cloud Computing Technologies for Green Enterprises

Green computing, also called green equipment, is the environmentally sustainable to use of computers and related resources like - monitors, printer, storage devices, networking and communication systems - effectively with minimal or no impact on the environment. Green cloud is a catchphrase that refers to the potential environmental benefits that information technology (IT) services delivered over the Internet will present society. The word combines the words green meaning environmentally gracious and cloud, the traditional image for online and the shortened name for a type of service delivery model known as cloud processing.
Blockchains

In recent years, the UK railway industry has struggled with the effects of poor integration of data across ICT systems, particularly when that data is being used across organizational boundaries. Technical progress is being made by the industry towards enabling data sharing, but an open issue remains around how the costs of gathering and maintaining pooled information can be fairly attributed across the stakeholders who draw on that shared resource. This issue is particularly significant in areas such as Remote Condition Monitoring, where the ability to analyse the network at a whole-systems level is being blocked by the business cases around the purchase of systems as silos. Blockchains are an emerging technology that have the potential to revolutionize the management of transactions in a number of industrial sectors. This chapter will address the outstanding issues around the fair attribution of costs and benefits of data sharing in the rail industry by proposing blockchains as a forth enabler of the rail data revolution, alongside ESB, ontology, and open data.
Hybrid Speech Steganography System using SS-RDWT with IPDP-MLE approach

Generally, defence applications need the high secured communication over the channel. Most of the defence-based communications are performed through the audio and speech signals in the secured environment. To provide security to these digital media and speech signals, considerably advanced technologies are implementing in the last two decades. However, digital media transmission over the secured and unsecured channels creating several big problems in defence data transfer applications due to equivalence of works, plasticity, ease of replication and unauthorized use in the digital format of data. The possible solution is to secure digital media by cover writing the speech signals by using speech steganography, so the digital media effectively hid over the speech. To overcome these problems, this article is mainly focussing on securing the speech and message through the hybrid speech steganography approach. This system is implemented with the spread spectrum-based redundant discrete wavelet transform (SS-RDWT) with additional pause removal properties. The input cover speech signal contains a larger size, and system requires the higher power usage, computational time and extra storage capacity. Thus, an intelligent pause detection protocol (IPDP) is implemented to remove the pauses and reduces the size of the speech signal. The performance of the IPDP protocol is improved by the maximum likelihood estimation (MLE) procedure. Finally, SS-RDWT speech steganography is implemented along with the IPDP-MLE-based pause removal mechanism. The simulation results show that the proposed method gives better performance against various noise attacks and improved the robustness, imperceptibility and security as compared to the conventional approaches.
Mari Mutare

Mari mutare is a transdisciplinary design research project about biocompatible prostheses inspired by the early Christian being called Green Man, a human-plant hybrid that represents the nature-culture continuum. These objects are intended to address human exceptionalism from a post-anthropocentric, feminist and queer perspective. The aim of Mari mutare is to explore the multiplicity of subjectivities in ourselves and, consequently, to influence the perception of others. Arising from the emerging field of synthetic biology, speculative design methodology supports this proposal and it materialises through transhackfeminist biopractices as tools for creating knowledge and projecting other possible futures. The experiments are conducted around the Petri dish as an epistemic object. The dish contains a symbiotic assembly of human and plant cells that interpenetrate, digest and partially assimilate while grazing the categories of kingdom, species, gender, culture and nature. While this process materialises, human subjects test their future limbs aided by an augmented reality (AR) filter, as a proxy for the physical reality, to hack into self-reflection and subjectivity, thus projecting themselves beyond the self. This project is currently a work-in-progress and is supported by Pro Helvetia, Hangar Barcelona (EU Biofriction programme), Utopiana Geneva and Hackuarium.
Criticality-Guided Deep Reinforcement Learning for Motion Planning

Real-time and efficient collision avoidance is still challenging for mobile robots running in dynamic and crowded environments. Recent research shows that deep reinforcement learning (DRL) provides a framework to plan collision-free trajectories efficiently. However, most of the current DRL-based methods focus on a fixed number of obstacles in the environments, which limits their applications. In this paper, we propose a learning-based model, Crit-LSTM-DRL, for a robot moving in environments with a variable number of obstacles. It combines an LSTM (Long Short-Term Memory) model and a value-based DRL model. Given the states of a set of obstacles, Crit-LSTM-DRL first sorts the obstacles according to their possible collision time to the robot and then feeds to the LSTM model to generate a fixed-size hidden state. Then, the value-based DRL model takes the hidden state and robot state as input to compute the value. Hence, at any time step, an action is selected that maximizes the value function defined in the DRL framework. Finally, we compare the performance of Crit-LSTM-DRL with a state-of-the-art DRL-based planning method that aims to deal with a variable number of obstacles. The simulation results show that the three models of Crit-LSTM-DRL can improve the success rate by 4%, 20.1%, and 3.8%, and reduce the collision rate by 35.5%, 75%, and 66.7%, respectively.
Expanded Field Theory. New Axioms, Laws and Consequences.

The present study attempts to expand the Classic Field Theory to a more general theory of the field. This more general field theory is named Expanded Field Theory. This new theory contains 2 new axioms and 8 laws. It predicts to include the gravitational field and other unknown and unexplored fields. As it is well known the Classic Field Theory is described mostly by Theory of Electromagnetic Field. The Electromagnetic Field is described by Maxwell’s laws (1864). The Maxwell’s laws are certified by a single axiom which claims that the movement of a vector E in a closed loop (div rot E = 0) is evenly. The author replaces this axiom with a new one, according to which the movement of a vector E in an open loop (div rot E ≠ 0) or an open vortex (div Vor E ≠ 0) is unevenly. If the vortex is in plane (2D), it is named a cross vortex. If the vortex is in volume (3D), it is named a longitudinal vortex. Something more- each vortex can be decelerating (div (VorE) &lt;0) or accelerating (div (VorE)&gt; 0). After the first axiom are obtained immediately 4 types of movements – cross vortex , which can be accelerating or decelerating and longitudinal vortex , which can also be accelerating or decelerating. The following results are obtained : evenly movement is replaced with unevenly movement (decelerating or accelerating); movement in a closed loop is replaced with movement in an open loop or vortex ; a cross vortex in 2D generates a longitudinal vortex in 3D through a special transformation and vice versa- the longitudinal vortex in 3D through another special transformation generates a cross vortex in 2D; the decelerating vortex emits primary vortices to environment, but the accelerating vortex sucks into the same primary vortices from environment ; the accelerating longitudinal vortices are attracted to one another, as the faster is inserted into the slower and thus form a funnel- this is the model of gravity funnel . It should be noted, in particular, the results of the second axiom. It claims that two complex (cross- longitudinal) vortex objects in 3D that work in one direction as one complementary pair, are existed simultaneously. This way they are obtained 2 pairs of complementary objects in both directions. As a final result are received many models with similar shapes and content. For example, the pair in one direction of complex complementary vortex objects is a model of the electron-proton chain, and in the opposite direction is an antiproton-positron chain model.
Investigation and Research on the Cultivation of Students' English Interests under Artificial Intelligence Technology

With the vigorous development of artificial intelligence, there are abundant types of learning resources and various learning methods. The use of digital resources in the context of the Internet in learning can stimulate students’ interest in English learning, improve their English listening, speaking, reading, and writing skills, and effectively improve the efficiency of junior high school English classroom learning. Different people have different learning effects in the same learning environment because their own qualities are certainly one factor, and the more important factor is the different attitudes of the learners, that is, whether the learners are interested in learning. Interest can arouse students’ curiosity and eager thirst for knowledge, and interest can promote students’ active learning, spontaneous learning, and diligent learning more than intelligence and skills. For the English learning of junior high school students, interest is the first condition for them to learn English well. Only when students are interested in English learning, can they actively participate in English learning, and gradually become more and more interested in the specific English learning process. Through the questionnaire survey of middle school students, we can understand their tendency to attribute to English learning performance in the process of English learning. This paper aims to investigate whether middle school students are more interested in English, the higher their English scores will be, and whether male and female students are different in their interest in English. In this way, in the process of English teaching, attribution theory can be used to combine students' reality and their own experience to guide and train students in attribution so that they can form correct attributions. At the same time, this paper has a certain guiding role for workers engaged in English education for middle school students. It can help them understand the attribution tendency of middle school students in the process of English learning and give middle school students correct attribution guidance and training, thus improving teachers’ English teaching performance.
Joint Parameter Estimation and Trajectory Tracking of Bounding Boxes

In multi-extended object tracking, parameters (e.g., extent) and trajectory are often determined independently. In this paper, we propose a joint parameter and trajectory (JPT) state and its integration into the Bayesian framework. This allows processing measurements that contain information about parameters and states. Examples of such measurements are bounding boxes given from an image processing algorithm. It is shown that this approach can consider correlations between states and parameters.In this paper, we present the JPT Bernoulli filter. Since parameters and state elements are considered in the weighting of the measurement data assignment hypotheses, the performance is higher than with the conventional Bernoulli filter. The JPT approach can be also used for other Bayes filters.
Statistical Features for Extractive Automatic Text Summarization

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.
Compressing the Bitcoin Blockchain using Incremental Snapshots

Blockchains are append-only data structures, meaning their size increases steadily. As of March 2021, new nodes connecting to the Bitcoin blockchain already need to synchronize and validate more than 330 GB of data. Already now, this is an issue for resource-constrained thin clients who lack the required download, storage, or computing capacities to achieve this. In future, this issue will become even more relevant when blockchain technology is applied to new domains like the Internet of Things (IoT). We propose a new blockchain architecture that addresses this issue. Our proposal relies on incremental snapshot blocks that allow new and occasionally used clients to synchronize with the current state of the blockchain efficiently. The snapshot blocks form a second linked blockchain and are secured by the blockchain consensus algorithm. In this paper, we describe the proposed architecture in detail and evaluate its storage-saving potential using the Bitcoin blockchain. The evaluations performed show that our design can reduce the volume of data that needs to be downloaded and stored by up to 98%, thus facilitating secure blockchain-based applications also on resource-constrained thin clients like smartphones, netbooks, or IoT devices.
Multi-sensor Distributed Estimation Fusion Based on Minimizing the Bhattacharyya Distance Sum

In multi-sensor distributed estimation fusion, local estimation errors are generally correlated among local estimates. Usually, correlation is known to exist but unavailable or unclear to be how large it is, and needed to be considered. For this situation, a sensible way is to set up an optimality criterion and optimize it over all possible such correlations. Based on the framework of minimizing the statistical distance sum between the fused density and local posterior densities, a new method is proposed by utilizing Bhattacharyya distance, which is commonly used in measuring the closeness or similarity between two densities. First, the objective function is introduced. Then, we investigate the convexity form of the objective function, and separate the solving procedure into two steps during settling the original optimization problem, which benefit us to acquire the solution of that problem. At last, the acquired solution (fused estimate) is given in an implicit form, however, it can be obtained through iterative algorithm. And, it is pessimistic definite in mean square error (MSE). Numerical examples illustrate this and show the effectiveness of the proposed distributed method by comparing with several other fusion methods under the same framework but using other kinds of statistical distance.