Reviewerly/exHarmony huggingface dataset:
Efficient and Adaptive Procurement Protocol with Purchasing Privacy

A procurement protocol is a protocol for a buyer to purchase digital goods at their prices from a vendor. A procurement protocol with privacy preservation can be achieved by priced oblivious transfer (POT). POT allows the buyer to obliviously procure items one by one. An adaptive POT protocol only consumes O(1) communication cost in each transaction, where all items are committed and encrypted before transactions. However, we found that the state-of-the-art adaptive POT protocol proposed by Rial et al. is less practical and does not meet real-world needs. It has to restrict to the one-buyer setting where all items are encrypted associated with one buyer's public key. For multiple buyers, the vendor must respectively encrypt all the same items for each buyer. Besides, it has to employ computationally expensive primitives such as zero-knowledge proof which imply inefficient computation operations. It is therefore unscalable and unsuitable in large-scale applications. In this paper, we propose an efficient adaptive priced oblivious transfer protocol to address the aforementioned problems. The proposed adaptive POT is built on top of a new cryptographic primitive, namely, adaptive set membership encryption (ASME). In our proposed protocol, all items are encrypted without the use of buyers' public keys and hence they can be used for universal buyers. Our protocol significantly reduces the transaction cost compared to existing schemes. For example, the communication in each transaction costs only 6 group elements compared to at least 141 group elements in Rial et al.'s protocol. The implementation shows that our protocol is efficient in terms of bandwidth and computational cost.
The Epistemology of Nondeterminism

This paper proposes new semantics for propositional dynamic logic (PDL), replacing the standard relational semantics. Under these new semantics, program execution is represented as fundamentally deterministic (i.e., functional), while nondeterminism emerges as an epistemic relationship between the agent and the system: intuitively, the nondeterministic outcomes of a given process are precisely those that cannot be ruled out in advance. We formalize these notions using topology and the framework of dynamic topological logic (DTL) (Kremer and Mints in Ann Pure Appl Logic 131:133–158, 2005). We show that DTL can be used to interpret the language of PDL in a manner that captures the intuition above, and moreover that continuous functions in this setting correspond exactly to deterministic processes. We also prove that certain axiomatizations of PDL remain sound and complete with respect to the corresponding classes of dynamic topological models. Finally, we extend the framework to incorporate knowledge using the machinery of subset space logic (Dabrowski et al. in Ann Pure Appl Logic 78:73–110, 1996), and show that the topological interpretation of public announcements as given in Bjorndahl (in: van Ditmarsch and Sandu (eds) Jaakko Hintikka on knowledge and game theoretical semantics, outstanding contributions to Logic, vol 12, Springer, 2018) coincides exactly with a natural interpretation of test programs.
Models for Computable Phenotyping

A computable phenotype is a set of computer-readable inclusion/exclusion criteria for a patient cohort. Such criteria should be specific and objective enough in order to turn them into a machine-readable query, yet also generalized enough to make them portable between different data sources; verbal descriptions are not computable phenotypes, no matter how readable they are to physicians, nor are a set of proprietary codes for a specific electronic health record system. This article describes the state of the art in computable phenotype models in the context of electronic health records, and illustrates their use with a use case in heart disease.
Italian Folk Multiplication Algorithm Is Indeed Better: It Is More Parallelizable

Traditionally, many ethnic groups had their own versions of arithmetic algorithms. Nowadays, most of these algorithms are studied mostly as pedagogical curiosities, as an interesting way to make arithmetic more exciting to the kids: by applying to their patriotic feelings—if they are studying the algorithms traditionally used by their ethnic group—or simply to their sense of curiosity. Somewhat surprisingly, we show that one of these algorithms—a traditional Italian multiplication algorithm—is actually in some reasonable sense better than the algorithm that we all normally use—namely, it is easier to parallelize.
Interval stabbing on the Automata Processor

The Automata Processor (AP) was designed for string-pattern matching. In this paper, we showcase its use to execute integer and floating-point comparisons and apply the same to accelerate interval stabbing queries. An interval stabbing query determines which of the intervals in a set overlap a query point. Such queries are often used in computational geometry, pattern matching, database management systems, and geographic information systems. The check for each interval is programmed as a single automaton and multiple automata are executed in parallel to provide significant performance gains. While handling 32-bit integers or single-precision floating-point numbers, up to 2.75 trillion comparisons can be executed per second, whereas 0.79 trillion comparisons per second can be completed for 64-bit integers or double-precision floating-point numbers. Additionally, our solution leaves the intervals in the set unordered allowing addition or deletion of an interval in constant time. This is not possible for contemporary solutions wherein the intervals are ordered, making update of intervals complex. Our automata designs are modular allowing them to become constituent parts of larger automata, where the numerical comparisons are part of the overall pattern matching operation. We have validated the designs on the hardware, and the routines to generate the necessary automata and execute them on the AP will be made available as software libraries shortly.
Real-time Dash Streaming Architecture for Internet of Things Using FBMRWP Model for Medical Videos

The proposed method uses random adjustments in the online video quality based on the bandwidth allocated over Dynamic Adaptive Streaming over HTTP (DASH) streaming service.The main objective is to improve the video quality from DASH-HTTP servers with variable bandwidth. Here, the system is adjusted dynamically for providing best video quality services based on the requirement of the user.In order to achieve such objective, the DASH service is assigned with three modules. Initially, the quality is adjusted dynamically using Fractional Brownian Motion and Random Waypoint Mobility (FBM-RWP) model. This initial model schedules the packets in sub-streams based on the priority as per the requirement of the user. The final model uses the Proportional Integral Derivative (PID) quality control algorithm for the past and future prediction of quality based on bandwidth allocation. This feedback of quality is used by the FBM-RWP model to prioritize the packets in the sub-streams. The entire process works by matching the bit rate of video streaming with the user required quality. Resuslts: The technique concentrates mostly on medical videos for improving the live video streaming in case of medical emergencies. The performance of the proposed method is compared with the conventional DASH services. The results proved that the proposed method performs better in terms of reduced error and improved throughput.
A formal framework for Stringology

A new formal framework for Stringology is proposed, which consists of a three-sorted logical theory S designed to capture the combinatorial reasoning about finite strings. We propose a language LS for expressing assertions about strings, and study in detail two sets of formulas Σ0B, a set of formulas decidable in polytime, and Σ1B, a set of formulas with the property that those provable in S yield polytime algorithms.
Knowledge and Reasoning

Research on knowledge representation and reasoning (KR&R) dates back to mathematical logic and is one of the foundations of Artificial Intelligence. Several data models and inference tools have been proposed in the literature, varying from general purpose languages to ad-hoc reasoning systems. Recently, bioinformatics ignited new interest in this research area. Indeed, several bioinformatics problems can be effectively solved via KR&R methods. In this article we provide an overview of KR&R, focusing on one important paradigm named Answer Set Programming (ASP). We also showcase some interesting bioinformatics problems solved with ASP.
Pedagogical Approach to Effective Cybersecurity Teaching

Initial research ruled out many factors that were thought may correlate to student academic performance. Finally, a strong correlation was found between their academic performance and their motivation. Following on from this research teaching practice was restructured to improve student motivation, engagement, and interest in cybersecurity by contextualizing teaching material with current real-world scenarios. This restructuring led to a very significant improvement in student academic performance, engagement and interest in cybersecurity. Students were found to attend more of their lectures and practical sessions and that this had a strong positive correlation with their academic performance.
Data-Information-Concept Continuum From a Text Mining Perspective

The recent Web panorama reveals a tangible proliferation of “social” data, in form of posts, opinions, feelings, experiences. Most of the available data is unstructured text, unsuitable to be processed by computers, especially due to ambiguity and vagueness of the natural language. Research developments highlight the difficulty in capturing semantics of terms, linguistic expressions, and sentences and their consequent representation as a finite concept. This article presents an open-minded overview of the Text Mining approaches, targeted at transforming unstructured textual data into explicit knowledge, with a special focus on the conceptualization, i.e., the concept identification by analysing syntactic and semantic relations among terms as well as the contextual surrounding information. Different knowledge granulation is described in a layered knowledge model, where the term, the information and the concept represent the basic knowledge granules that cover most Text Mining approaches, in an evolving knowledge continuum.
Physical-barrier detection based collective motion analysis

Collective motion is one of the most fascinating phenomena and mainly caused by the interactions between individuals. Physical-barriers, as the particular facilities which divide the crowd into different lanes, greatly affect the measurement of such interactions. In this paper we propose the physical-barrier detection based collective motion analysis (PDCMA) approach. The main idea is that the interaction between spatially adjacent pedestrians actually does not exist if they are separated by the physical-barrier. Firstly, the physical-barriers are extracted by two-stage clustering. The scene is automatically divided into several motion regions. Secondly, local region collectiveness is calculated to represent the interactions between pedestrians in each region. Finally, extensive evaluations use the three typical methods, i.e., the PDCMA, the Collectiveness, and the average normalized Velocity, to show the efficiency and efficacy of our approach in the scenes with and without physical barriers. Moreover, several escalator scenes are selected as the typical physical-barrier test scenes to demonstrate the performance of our approach. Compared with the current collective motion analysis methods, our approach better adapts to the scenes with physical barriers.
Polyscale gradients residual demosaicking

Image acquisition through a single-sensor camera captures only a fraction of the image data renowned as the raw image. It uses the colour filter array to acquire only a single colour channel in each pixel location. The reconstruction of the so called raw image into a full colour image is known as demosaicking or interpolation. Demosaicking is a significant process in the imaging pipeline. It implicates the reconstruction of the missing two colour channels from an incomplete image obtained from a single-sensor digital camera. This paper proposes a new approach of combining polyscale gradients algorithm and residual demosaicking. The first phase utilizes the gradients at different scales and residual demosaicking (RD) involves subtraction of the pragmatic and the provisional pixel values in the residual province takes place in the second phase. The experimental results show that the proposed demosaicking algorithm outperforms the conventional demosaicking algorithms for the Kodak and McMaster (McM) dataset.
Algorithms for Strings and Sequences: Pairwise Alignment

Pairwise alignment between two biological sequences, such as DNA, RNA, or proteins, is a classical and well studied bioinformatic problem. In fact, this latter problem can be found in many biological analyses such as those involving data coming from sequencing processes. In this work we will formalize the computational problem, and we will present the two most common variants, which are the global and the local versions. More precisely, we will describe the dynamic programming algorithm proposed by Needleman and Wunsch for finding the optimal global pairwise alignment between two strings with respect to a given scoring function, and also the algorithm of Smith and Waterman for the local version. Here, we will provide the details of both the algorithmic procedures and some examples showing their usage in reconstructing optimal alignments. Finally, we will also discuss some alternative scoring functions that are used in practice to deal with the pairwise alignment of biological sequences.
The Challenge of Privacy in the Cloud

The cloud is the most effective way to allow collaborative work and to share information among multiple parties, possibly spread out over the world. Health data are an excellent example of this need, because sharing them can drastically improve analysis of pathologies, therapies, and protocols, even for the daily doctor’s activity. Unfortunately, no guarantee about the trustworthiness of the cloud provider is given. Therefore, the protection of data of users and organizations is a relevant problem. This article focuses on this topic, by analyzing the main issues arising when sensitive data are managed by the cloud. Among the others, the specific problem of access linkage is addressed in detail. Indeed, a clinical service might require the cloud to allow only anonymous (yet accountable) accesses, to protect privacy. Unfortunately, this is not enough, because, by linking the critical access of a user to another access in which processed data can lead to the user being identified (for example, a ticket service), the cloud can identify the user and thus break the privacy of the health service. To face this problem, we propose an authentication scheme supporting anonymity of users and unlinkability of service accesses. This goal is reached by combining a multi-party cryptographic protocol with the use of a cooperative P2P-based approach to access services in the cloud. Nevertheless, accountability of user access is always preserved, to prevent misuse and untraceable illegal actions.
Self-learning Framework with Temporal Filtering for Robust Maritime Vessel Detection

With the recent development in ConvNet-based detectors, a successful solution for vessel detection becomes possible. However, it is essential to access a comprehensive annotated training set from different maritime environments. Creating such a dataset is expensive and time consuming. To automate this process, this paper proposes a novel self learning framework which automatically finetunes a generic pre-trained model to any new environment. With this, the framework enables automated labeling of new dataset types. The method first explores the video frames captured from a new target environment to generate the candidate vessel samples. Afterwards, it exploits a temporal filtering concept to verify the correctly generated candidates as new labels for learning, while removing the false positives. Finally, the system updates the vessel model using the provided self-learning dataset. Experimental results on our real-world evaluation dataset show that generalizing a finetuned Single Shot Detector to a new target domain using the proposed self-learning framework increases the average precision and the F1-score by 12% and 5%, respectively. Additionally, the proposed temporal filter reduced the noisy detections in a sensitive setting from 58% to only 5%.
What matters more: The size of the corpora or their quality?

This study investigates (and compares) the impact of the size and the similarity/quality of comparable corpora on the specific task of extracting translation equivalents of verb-noun collocations from such corpora. The comprehensive evaluation of different configurations of English and Spanish corpora sheds some light on the more general and perennial question: what matters more – the quantity or quality of corpora?
Towards Deadline Guaranteed Cloud Storage Services

More and more organizations move their data and workload to commercial cloud storage systems. However, the multiplexing and sharing of the resources in a cloud storage system present unpredictable data access latency to tenants, which may make online data-intensive applications unable to satisfy their deadline requirements. Thus, it is important for cloud storage systems to provide deadline guaranteed services. In this paper, to meet a current form of service level objective (SLO) that constrains the percentage of each tenant's data access requests failing to meet its required deadline below a given threshold, we build a mathematical model to derive the upper bound of acceptable request arrival rate on each server. We then propose a Deadline Guaranteed storage service (called DGCloud) that incorporates three basic algorithms. Its deadline-aware load balancing scheme redirects requests and creates replicas to release the excess load of each server beyond the derived upper bound. Its workload consolidation algorithm tries to maximally reduce servers while still satisfying the SLO to maximize the resource utilization. Its data placement optimization algorithm re-schedules the data placement to minimize the transmission cost of data replication. We further propose three enhancement methods to further improve the performance of DGCloud. A dynamic load balancing method allows an overloaded server to quickly offload its excess workload. A data request queue improvement method sets different priorities to the data responses in a server's queue so that more requests can satisfy the SLO requirement. A wakeup server selection method selects a sleeping server that stores more popular data to wake up, which allows it to handle more data requests. Our trace-driven experiments in simulation and Amazon EC2 show the superior performance of DGCloud compared with previous methods in terms of deadline guarantees and system resource utilization, and the effectiveness of its individual algorithms.
Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELCO
Impact of Big Data on Security

Big data has great commercial importance to major businesses, but security and privacy challenges are also daunting this storage, processing, and communication. Big data encapsulate organizations' most important and sensitive data with multi-level complex implementation. The challenge for any organization is securing access to the data while allowing end user to extract valuable insights. Unregulated access privileges to the big data leads to loss or theft of valuable and sensitive. Privilege escalation leads to insider threats. Also, the computing architecture of big data is not focusing on session recording; therefore, it is becoming a challenge to identify potential security issues and to take remedial and mitigation mechanisms. Therefore, various big data security issues and their defense mechanisms are discussed in this chapter.
Feedback Analysis of Information Teaching System Based on Web Database

In order to further dynamically update and maintain teaching resources and establish a teaching platform with various interactive modes, which is suitable for students, teachers, management and other levels to operate comprehensively, this paper constructs a Web-based experiment information teachin
Teaching Design of Fundamentals of College Computer Course Based on Mind Map with 3w2h

The knowledge of fundamentals of college computer is wide and fragmentary. How to make students understand and master numerous and complicated knowledge is the key problem in teaching. Mind map is a powerful tool to solve this problem. For this reason, according to the law of knowledge learning and
Bibliometric Synthesis of Research on Aesthetic Education for Middle School

This study aims to investigate the current state and trends in aesthetic education for middle school students through bibliometric analysis. Utilizing the Bibliometrics package in the R programming language, we analyze a dataset comprising 111 documents published from 1970 to 2023. This analysis off
Research on Teacher Evaluation Based on Artificial Intelligence and Big Data

Big data focuses on multi-dimensional, in-depth mining and scientific analysis of a large amount of data, discovering the hidden relationship and value behind the data, which helps the evaluation of teaching quality shift from speculation based on small-sample data or fragmented information to evide
Construction and Application of Financial Management Network Teaching System in Colleges and Universities under Python Language

Online education platforms are integral to educational reforms. This web-based teaching system embodies online education functionalities and process management. Using front-end Vue/React and back-end Python with Django, it includes user and course management modules. ORM technology accesses database
Javaweb Construction of the Management Information System for Township Health Centers under the Technology

Recently, the state attaches great importance to the development of primary medical undertakings, and it is urgent to develop the management information system suitable for township health centers. This study uses Javaweb technology to design and realize the management information system for townshi
Development and Application of College Music Massive Open Online Course Teaching Platform Based on Web Technology

With the deepening of "Internet+Education" mode, massive open online course, as a product of information education, has attracted wide attention at home and abroad, and further promoted the deep integration of digital education platform and traditional music education in colleges and universities. I
Research on the Development of College English E-learning Platforms in the Context of 'Internet +

College English teaching in China faces challenges like heavy workloads and monotonous teaching methods, requiring an urgent transformation. This study, based on "Internet +", employs needs analysis and platform modeling to construct an informatized teaching platform for college English. Integrating