Reviewerly/exHarmony huggingface dataset:
Evaluating Richer Features and Varied Machine Learning Models for Subjectivity Classification of Book Review Sentences in Portuguese

Texts published on social media have been a valuable source of information for companies and users, as the analysis of this data helps improving/selecting products and services of interest. Due to the huge amount of data, techniques for automatically analyzing user opinions are necessary. The research field that investigates these techniques is called sentiment analysis. This paper focuses specifically on the task of subjectivity classification, which aims to predict whether a text passage conveys an opinion. We report the study and comparison of machine learning methods of different paradigms to perform subjectivity classification of book review sentences in Portuguese, which have shown to be a challenging domain in the area. Specifically, we explore richer features for the task, using several lexical, centrality-based and discourse features. We show the contributions of the different feature sets and evidence that the combination of lexical, centrality-based and discourse features produce better results than any of the feature sets individually. Additionally, by analyzing the achieved results and the acquired knowledge by some symbolic machine learning methods, we show that some discourse relations may clearly signal subjectivity. Our corpus annotation also reveals some distinctive discourse structuring patterns for sentence subjectivity.
The research and application of trusted startup of embedded TPM

In view of the security threats caused by the code execution vulnerability of the industrial control system, design the trusted security architecture of the industrial control system based on the embedded system. From the trusted startup of industrial control equipment, the safety protection for industrial control system is completed. The scheme is based on TPM and Xilinx Zynq-7030 to build an industrial trusted computing environment and complete the trusted startup process. Experiment shows that this method can effectively prevent the destruction of malicious code during the startup process of embedded system and provide technical support for the construction of trusted computing environment of industrial control system.
Text Classification Using K-Nearest Neighbor Algorithm and Firefly Algorithm for Text Feature Selection

Recently, the text classification is deliberated as the essential technique to handle a huge volume of unstructured documents. In general, this technique is used in the fields of information retrieval, text summarization, and text extraction. Feature selection is the important process in the text classification system. It is used to select the subset from the original set of features. The high-dimensional set of features will upturn the complexity of the text classification task. In order to reduce the dimensionality of the huge volume of text documents, this research work proposes a new firefly algorithm for feature selection. Here, the firefly algorithm is used to optimize the set of high-dimensional features. Next, the classification task will be performed by using the selected features. The performance of the proposed algorithm is compared with the widely used feature selection techniques such as information gain and χ2 statistic. From the experimental results, the firefly feature selection algorithm performs well when compared to other methods.
Differentially Private Actor and Its Eligibility Trace

We present a differentially private actor and its eligibility trace in an actor-critic approach, wherein an actor takes actions directly interacting with an environment; however, the critic estimates only the state values that are obtained through bootstrapping. In other words, the actor reflects the more detailed information about the sequence of taken actions on its parameter than the critic. Moreover, their corresponding eligibility traces have the same properties. Therefore, it is necessary to preserve the privacy of an actor and its eligibility trace while training on private or sensitive data. In this paper, we confirm the applicability of differential privacy methods to the actors updated using the policy gradient algorithm and discuss the advantages of such an approach with regard to differentially private critic learning. In addition, we measured the cosine similarity between the differentially private applied eligibility trace and the non-differentially private eligibility trace to analyze whether their anonymity is appropriately protected in the differentially private actor or the critic. We conducted the experiments considering two synthetic examples imitating real-world problems in medical and autonomous navigation domains, and the results confirmed the feasibility of the proposed method.
On Freedom in the Artificial Age

The currently ongoing introduction of Artificial Intelligence (AI), robotics and big data into our contemporary society causes a market transformation that heightens the need for ethics in the wake of an unprecedented outsourcing decision making to machines. Artificial Intelligence (AI) poses historically unique challenges for humankind. This chapter will address legal, economic and societal trends in the contemporary introduction of Artificial Intelligence (AI), Robotics and Big Data derived inferences. In a world, where there is a currently ongoing blend between human beings and AI, the emerging autonomy of AI holds unique potentials of eternal life but also imposes pressing legal and ethical challenges in light of AI gaining citizenship, overpopulation concerns and international development gaps. The current legal status of AI and robotics will be outlined with special attention to consumer protection and ethics in the healthcare sector. The unprecedented economic market revolution of outsourced decision making to AI will be captured in macroeconomic trends outlining AI as corruption free market solution, which is yet only prevalent and efficient in some parts of the world. Finally, a future-oriented perspective on the use of AI for enhancing democracy and diplomacy will be granted but also ethical boundaries envisioned. The mentioned transition appears to hold novel and unprecedentedly-described freedom challenges in our contemporary world.
Adaptive quantum state estimation for dynamic quantum states

We propose an adaptive quantum state estimation method for nonstationary quantum states and verify the method by numerical simulations and experimental investigations. Adaptive quantum state estimation provides an asymptotically optimal scheme for estimating an unknown input quantum state by updating the measurement configuration upon the detection of each quantum. However, previous methods are only valid for quanta with the same quantum state (a stationary state). By adopting the likelihood function for a fixed number of recent detection results, our sequential adaptive quantum state estimation allows quantum states changing in time to be estimated. The numerical simulation results and experimental demonstration using photons agree well with the theoretical predictions. This method will find applications in various fields where dynamically changing quantum states need to be estimated.
Pedestrian Multi-target Tracking Based on YOLOv3

Pedestrian multi-object tracking is an important research direction in the fields of computer vision which has been widely applied in intelligent surveillance, human-computer interaction and so on. The traditional method has high computation complexity, slow detection speed and cannot track in real time. To solve this problem, a multi-object tracking method based on YOLOv3 is proposed. It makes use of the accuracy and timeliness of YOLOv3 in detection, and combines with an efficient data association method to track pedestrian targets in real time. More discriminating pedestrian re-identification network is introduced in order to reduce ID switches in tracking, and it alleviates the problem of long term occlusion. The experimental results on datasets show that several indexes have been improved and the proposed method can better adapt to complex scenes and extract more complete target trajectories.
Micro-mobilities in curated spaces: agency, autonomy and dwelling in visitor experiences of augmented reality in arts and heritage

Mobile technologies are transforming the ways in which we experience arts and heritage sites, and galleries and museums are facing increased pressure to provide stimulating, alternative technology-based solutions for enriching visitor experiences. Focusing on the opportunities afforded by augmented reality (AR), this paper critiques the role this technology plays in providing visitors the opportunity to experience art and exhibitions through a series of dynamic, small-scale micro-mobilities. We propose that AR creates curated spaces of mobility in galleries and museums and in doing so, visitors become empowered through spaces of agency, autonomy and dwelling as they negotiate these spaces and encounter art through technology-mediated forms of wayfinding, interpretation and personal curation. Through negotiated agencies of human and non-human, visitors become emancipated, active agents in a process of co-production. Such positioning is further critiqued as the paper investigates the opportunities afforded by augmented reality to create alternative spaces of connection and interpretation through conceptualisations of dwelling and we suggest technology holds the potential to facilitate an enriched, deeper and more personal connection to that experienced in art gallery and exhibition spaces.
Virtual supervision of pharmacy undergraduate research projects during the COVID-19 lockdown in Zimbabwe

&#x0D; &#x0D; &#x0D; &#x0D; The coronavirus 2019 (COVID-19) pandemic has led to national lockdowns in multiple states. The education system, including pharmacy education, has been negatively affected by the subsequent closure of universities. Many institutions have resorted to virtual learning, which work best in settings with fast, stable internet connections and where electricity is readily available. In resource-limited settings, popular virtual interaction platforms like Zoom may not work, and educators have to devise novel ways to continue teaching. The University of Zimbabwe used innovative mobile phone-based processes to ensure continuation of the final-year pharmacy students’ honours research projects, supervision, and timely submission. This facilitated rapid communication between students and professors and allowed effective guidance for the students’ research process. All students completed their projects; 13 out of 16 submitted their write-ups within the deadline; all students passed, 69% scored an upper second (2.1) grade or better, indicating the high quality of the majority of the research projects.&#x0D; &#x0D; &#x0D; &#x0D;
Isolated Trees in Multi-Tenant Fat Tree Datacenters for In-Network Computing

Modern datacenter applications do not only require computational power but also a high amount of communication between computation nodes. In order to improve the running time of these applications, the network community has been seeking for offloading common computation patterns to the network devices, an emerging field known as In-Network Compute. Artificial intelligence (AI) constitutes an important application obtaining a major performance improvement from in-network compute due to its distributive inherent properties. Most of existing in-network compute schemes are based on the employment of aggregation trees providing a logical tree topology on top of the physical infrastructure for the different aggregation nodes. In this work, we study the fundamental question of implementing such logical trees in an efficient manner. In order to avoid interference among tenants, trees serving various tenants should be edge-disjoint. We study designing such trees in common datacenter topologies such as fat trees. We show that the availability of the trees is highly affected by the mapping of the tenants to hosts of the topology. We describe algorithms for computing mapping that allows the existence of such trees as well as for finding such trees for a given tenant mapping. We conduct experiments using real workloads to examine trees availability based on network utilization and particular mappings.
Approximation of Bayesian models for time-to-event data

Random measures are the key ingredient for effective nonparametric Bayesian modeling of time-to-event data.This paper focuses on priors for the hazard rate function, a popular choice being the kernel mixture with respect to a gamma random measure.Sampling schemes are usually based on approximations of the underlying random measure, both a priori and conditionally on the data.Our main goal is the quantification of approximation errors through the Wasserstein distance.Though easy to simulate, the Wasserstein distance is generally difficult to evaluate, making tractable and informative bounds essential.Here we accomplish this task on the wider class of completely random measures, yielding a measure of discrepancy between many noteworthy random measures, including the gamma, generalized gamma and beta families.By specializing these results to gamma kernel mixtures, we achieve upper and lower bounds for the Wasserstein distance between hazard rates, cumulative hazard rates and survival functions.
Hybrid Framework of Convolution and Recurrent Neural Networks for Text Classification

Convolutional neural network (CNN) and recurrent neural network (RNN) are two popular architectures used in text classification. Traditional methods to combine the strengths of the two networks rely on streamlining them or concatenating features extracted from them. In this paper, we propose a novel method to keep the strengths of the two networks to a great extent. In the proposed model, a convolutional neural network is applied to learn a 2D weight matrix where each row reflects the importance of each word from different aspects. Meanwhile, we use a bidirectional RNN to process each word and employ a neural tensor layer that fuses forward and backward hidden states to get word representations. In the end, the weight matrix and word representations are combined to obtain the representation in a 2D matrix form for the text. We carry out experiments on a number of datasets for text classification. The experimental results confirm the effectiveness of the proposed method.
ACCTP: Cross Chain Transaction Platform for High-Value Assets

Recently, with the development of blockchain technology, people not only regard blockchain as a tradable cryptocurrency but also pay attention to other areas where blockchain technology can be applied, such as high-value assets (art, diamonds, etc.). Through the study of some existing blockchain-based platforms for high-value assets, we find that the blockchain ecosystem in this emerging industry presents a state of blooming but isolated from each other. In view of the problem, we discuss some projects based on cross-chain technology and believe that using cross-chain technology is one of the best solutions. Therefore, we propose the ACCTP (Asset Cross Chain Transaction Platform) model to connect different blockchain-based transaction platforms. The model is divided into three cross-chain service layers, called C2IE, C2AE, and C2TS, which are designed to implement the functions of information exchange, asset exchange and transaction exchange respectively.
Post-Quantum zk-SNARK for Arithmetic Circuits using QAPs

In recent years, the zero-knowledge proof and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) have drawn significant attention as privacy-enhancing technologies in various domains, especially the cryptocurrency industry and verifiable computations. A post-quantum designated verifier type zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) for Boolean circuits was proposed by Gennaro et al. in ACM CCS `18. However, this scheme does not include arithmetic circuits. Furthermore, it is difficult to use it in various applications. Their paper described the construction of a post-quantum designated verifier zk-SNARK for arithmetic circuits from quadratic arithmetic programs (QAPs) as an open problem. Recently, Nitulescu proposed a post-quantum designated verifier zk-SNARK for arithmetic circuits using square arithmetic programs (SAPs), which are the special cases of QAPs.In this paper, we give another answer to this problem and propose a post-quantum designated verifier zk-SNARK scheme for arithmetic circuits using QAPs. Our proposal, which employs QAPs, the zero-knowledge proof comprises three learning with errors (LWE) ciphertexts. We implemented our proposed scheme and the other known schemes using the libsnark library. Our experimental results show that our scheme can generate a zero-knowledge proof, which is known as the bottleneck of zk-SNARK, for an arithmetic circuit that comprises 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> gates in a processing time of only 50 s, which is approximately three times faster than that of the post-quantum zk-SNARKs by Gennaro et al. or two times faster than the one by Nitulescu.
A Canny operator road edge detection method based on color features

Abstract The road edge detection method, proposed by us, highlights the morphological and color features of the road in the image. The method performs color space conversion and color feature screening on color images containing road information, and Canny operator is used for edge detection. Then the edge detection results are detected by straight lines, and the road edges are detected and marked based on the principle of Hough transform. The experiment of edge detection on road pictures with complex background proves that the detection method can better detect the road edge. The novelty of this method is to combine several principles such as color feature screening, Canny operator and Hough transform. Therefore, a new method of road edge detection is formed.
Alpine Skiing Trajectory Optimization Based on Radau Pseudospectral Method

In this paper, the trajectory optimization of alpine skiing is studied based on Radau pseudospectral method (RPM). First, the kinetic model of alpine skiing is established by Newton's second law, then the boundary constraints are considered, and the trajectory optimization of alpine skiing is converted into the optimal control problem which takes the minimum time consumption as the cost function. Finally, the optimal control problem is transcribed to a nonlinear programming problem (NLP) by RPM, and the optimal solution is obtained. Simulation results show that RPM can effectively generate a continuous optimal path meet the requirement of accuracy.
Learning Driver Behaviors Using A Gaussian Process Augmented State-Space Model

An inference method for Gaussian process augmented state-space models are presented. This class of grey-box models enables domain knowledge to be incorporated in the inference process to guarantee a minimum of performance, still they are flexible enough to permit learning of partially unknown model dynamics and inputs. To facilitate online (recursive) inference of the model a sparse approximation of the Gaussian process based upon inducing points is presented. To illustrate the application of the model and the inference method, an example where it is used to track the position and learn the behavior of a set of cars passing through an intersection, is presented. Compared to the case when only the state-space model is used, the use of the augmented state-space model gives both a reduced estimation error and bias.
A Blockchain-Based Privacy-Preserving Mechanism for Attribute Matching in Social Networks

Online social network has become an indispensable part of people’s life, and people are willing to make friends in social network. However, semi-honest social network platforms may reveal users’ private information, which will cause great losses to users. Existing schemes either are based on Secure Multi-party Computation (SMC), which have a lot of resource consumption, or are based on vector dot product calculation, which are vulnerable to statistical attacks. In this paper, we propose an attribute matching scheme based on hierarchical blockchain and Ciphertext Policy Attribute-based Encryption (CP-ABE), which can realize attribute matching in semi-honest servers and offer fine-grained attributes selection for users to get more precise matching. Additionally, a novel blockchain architecture is proposed in our scheme, which reduces the storage consumption and improve running efficiency. Finally, we present the security analysis that the scheme can resist a variety of attacks, and the experiment results demonstrate the feasibility of the scheme.
Project FORESIGHT: A ten-year retrospective

Forensic service providers fulfill a fundamental role in a criminal justice system by providing scientific information that aids investigations and court proceedings. While the focus is often on the science aspect of these organizations, the provision is also of paramount importance. Historically, calls for more and better information about forensic laboratory performance (in essence, benchmarking) have gone unheard. Project FORESIGHT, created in 2008, filled this need through engagement with the forensic management community to build a needs-based process for providing operational data that can be used to enhance a laboratory's performance. With over 10 years of industry data, Project FORESIGHT is the de facto standard for benchmarking forensic service provision.
Ownership at Large

Software-intensive organizations rely on large numbers of software assets of different types, e.g., source-code files, tables in the data warehouse, and software configurations. Who is the most suitable owner of a given asset changes over time, e.g., due to reorganization and individual function changes. New forms of automation can help suggest more suitable owners for any given asset at a given point in time. By such efforts on ownership health, accountability of ownership is increased. The problem of finding the most suitable owners for an asset is essentially a program comprehension problem: how do we automatically determine who would be best placed to understand, maintain, evolve (and thereby assume ownership of) a given asset. This paper introduces the Facebook Ownesty system, which uses a combination of ultra large scale data mining and machine learning and has been deployed at Facebook as part of the company's ownership management approach. Ownesty processes many millions of software assets (e.g., source-code files) and it takes into account workflow and organizational aspects. The paper sets out open problems and challenges on ownership for the research community with advances expected from the fields of software engineering, programming languages, and machine learning.