Reviewerly/exHarmony huggingface dataset:
Operational Annotations

I present a new method for specifying and verifying the partial correctness of sequential programs. The key observation is that, in Hoare logic, assertions are used as selectors of states, that is, an assertion specifies the set of program states that satisfy the assertion. Hence, the usual meaning of the partial correctness Hoare triple $$\{f\}\,P \,\{g\}$$ : if execution is started in any of the states that satisfy assertion f, then, upon termination, the resulting state will be some state that satisfies assertion g. There are of course other ways to specify a set of states. Given a program $$\alpha $$ , the post-states of $$\alpha $$ are the states that $$\alpha $$ may terminate in, given that $$\alpha $$ starts executing in an arbitrary initial state. I introduce the operational triple $$[\alpha ]\,P \,[\beta ]$$ to mean: if execution of $$P $$ is started in any post-state of $$\alpha $$ , then upon termination, the resulting state will be some post-state of $$\beta $$ . Here, $$\alpha $$ is the pre-program, and plays the role of a pre-condition, and $$\beta $$ is the post-program, and plays the role of a post-condition.
Improved R-Cnn: Improving Feature Representation for Object Detection

Object detection has achieved excellent success benefits from feature pyramid architecture in recent years. However, the multi-scale and multi-level features are not fully exploited. In this paper, we introduce a new feature pyramid architecture named Improved R-CNN, which narrows the semantic gap between the deeper and shallow layers and reduces information loss. First, we propose a Residual-Fusing Feature Augmentation module for the highest level features to extract different spatial context information. Second, a Splitting-Fusing Feature Enhancement module is introduced to mitigate the semantic gaps between different level features. Finally, a Shrinkage L1 Loss is designed to balance the gradient of samples towards the optimal convergence. Owning to these three designs, our methods achieve 2.1 points and 1.4 points higher Average Precision (AP) on MS COCO with ResNet50-FPN and ResNet101-FPN as baseline respectively.
Weakly Supervised Salient Object Detection with Box Annotation

Box annotation is cost-friendly compared with pixel-wise dense labeling, more stable than scribbles, and contains more object information than image-level labels. However, recent approaches focus on scribble and image-level labels in weakly-supervised salient object detection (WSSOD) while ignoring box annotation. We first identify the reasons for the above ignorance and find the difficulties in describing the saliency and object contour from the box. To deal with the problems, we propose to make full use of box annotation by introducing three supervisions: vector supervision, point supervision, and plane supervision. We obtain the foreground by vector supervision, add constraints on points predictions in the background, and model the boundary information by learning the pair-wise relations in the image plane. Then, we provide a SOD dataset with box annotation and use our method to train the benchmark model on the dataset. At last, we show results on six benchmark datasets of salient object detection. We achieve the current best weakly-supervised performance and comparable performance with fully-supervised state-of-the-arts, suggesting that the gap between the weakly-supervised and fully-supervised salient object detection is closed by box annotation.
Applying Service Design to Online Learning to Increase User Experience

Advanced Internet development and the impact of COVID-19 have made online learning the most convenient way for learners to acquire knowledge. Online courses enable learners to communicate directly with teachers one-on-one and achieve mutual learning and real-time interaction. However, some instructors and learners might encounter operational difficulties during online learning. For example, learners might suffer from a frustrating emotion after failing to complete a series of tasks by operating the online learning platform since they are worried about making mistakes during the class. Moreover, not every person is familiar with the online learning software; it is imperative to establish guidelines for teachers and students to realize the software operation pattern before the class starts and improve students' self-confidence during the online course. Therefore, this article aimed to understand learners' pain points while using the online communication software-Google Meet during the online course and applied design services approaches to ideate the problems and conclude the suggested operational guideline. Accordingly, this article applied service design tools, including user journey map, how might we (HMWs) method to improve user experience and ultimately achieve an obstacles-free online learning environment.
Big Data Entrepreneurship in Healthcare

This case study describes the entrepreneurial process of Jiangsu Dongzhi Data Technology Co., Ltd (Dongzhi Data), from conception through implementation—from establishing a patient-centered healthcare model from the perspective of the patient health experience, finding an entrepreneurial inspiration, conceiving the entrepreneurial scheme, proposing the construction of a regional health information platform based on “health records”, and, finally, identifying the optimum entrepreneurial resource allocation during the survival period in two stages of implementation. Through this study and discussion, readers can understand Dongzhi Data’s entrepreneurial process better, and grasp how entrepreneurial companies look for entrepreneurial opportunities and survive the entrepreneurial lifecycle. They will also learn how to allocate significant strategic resources, such as talent, capital, environment, opportunity identification, and technology, during the survival period. Moreover, this case will give readers an enhanced understanding of the entrepreneurial process for small- and medium-sized enterprises in the big data industry and a strong theoretical and practical foundation for future entrepreneurial development.
ERAKS—Interactive Interface to Annotate Entities, Co-references, and Relationships

This paper aims to build a tool/an interface for automatic recognition of entities in a literary text and the relationships between them. The result of the practical work of three series master students of natural language processing was the creation of a manually annotated corpus from the Romanian version of the novel Quo Vadis by Henryk Sienkiewicz. It was previously annotated with a POS-tagger and received the sentences and words id. Entities, human characters, or gods, as well as geographic locations, were then annotated in XML format, then co-references between them sent to a common trunk. Afterwords, affect, social, and kinship relationship between these entities has also been annotated. Entities participating in a relationship have been annotated along with triggers, words in the text that are markers of that type of relationship. A tool was developed, which automatically searched triggers in a text annotated with more types of entities and co-references. Next, we searched with the same tool affect triggers in a chat corpus, i.e. diagnostic words for affect relationships between real characters. Further, we switched to the development of the tool, transformed into an interactive interface which annotates entities, then searches for triggers, and annotates them if they are validated by the human assistant. This interface can be used to annotate any text with real or fictional characters.
Transformation of Modern University Under Conditions of Global Challenges and Changes of Professional Environment

The basis for strategic positioning of modern university under conditions of global challenges and necessity to provide compliance with requirements of the professional environment is defined by active interaction of three factor groups: high level of focusing of intellectual and artistic potential of teaching personnel and students, proper resourcing to maintain favorable educational environment for personal and professional development and carrying out perspective fundamental and applied researches, and development and building of efficient management tool based on principles of novelty, flexibility, and strategic vision. The main purpose of this research is to reveal the factors that form preconditions for successful transformation of universities in Russia under conditions of global challenges and changes in professional environment into strategic centers for national development. The methodology for the research is based on the context of adaptation to changing outer conditions and providing of multidirectional interaction with professional environment with the purpose of meeting its current and further demands tied with functioning and development. In the process of modeling, the impact of global and domestic trends on development of the system of higher education is taken into account. The result of the research had to be determination of strategic vision that allows a university to perform successfully the role of expansion source, which is aimed to boost social development and space for building ecosystems for free collaboration of scientists who initiate cutting-edge researches and developments.
Live TV Streaming Latency Measurement Using YOLO

This paper proposes a simple yet effective approach to measure the e2e latency in the live video streaming pipeline, from when the signal is generated in the production studios until it is played on the client device. The method is based on user-centric behavior by looking at the time the content is produced in the source context and comparing it with the current clock time at the playback device. Given a clock timestamp introduced in the signal at the production stage, we rely on an intelligent streaming latency measurement agent that, first, detects with YOLO that mark at the playout device, then use an OCR to convert the bitmap text in the clock to a string text, and finally, compares it with the real-time clock in the machine, providing real-time end to end streaming latency. Our method, even simple, allows us to measure the latency on any playout device, as it does not rely on any in-band signaling but a human-centric behavior simulated by an intelligent measurement agent.
A Survey on Authentication Issues for the Internet of Things

IoT devices play much more important roles for our life and work. However, the security has become a severe problem. This paper focus on the authentication schemes on IoT framework different layers which include physical security schemes and protocol schemes. The physical security schemes use the characteristics which are unique and not easy to tamper for key generation and device identity authentication while the protocol schemes employ the Elliptic curve encryption (ECC) or such algorithm which is lightweight for identity encryption. Moreover, this paper presents the blockchain technology which is popular for device authentication due to the decentralization and traceability. Artificial intelligence has penetrated into many fields of industry, and there will be many researches in the field of authentication security. This paper give the prospect that more work should be carried on by the Ai technology.
APPLICATION OF MULTIHARMONIC NUMBERS FOR THE SYNTHESIS OF CLOSED FORMS OF PARAMETRICALLY MODIFIED FACTORIAL GENERATING SEQUENCES

A toolkit and a method for reducing sequences of integers belonging to the class of factorial-generating recursions to a closed form are presented. The signs and properties of the modified factorial-generating recursion of one and two variables are determined. The best-known factorial-generating recursion of two variables is the sequence of Stirling numbers of the first kind. Modified hyperharmonic numbers are used to synthesize an analytical recursion model. The advantages of these numbers for constructing closed forms of factorial-generating recursions are revealed. An incomplete closed form of the sequence of Stirling numbers of the first kind is synthesized.
A Blockchain-Based Fine-Grained Data Sharing Scheme for E-Healthcare System

The cloud-aided sharing of e-healthcare data has great positive significance for research. However, due to the privacy consideration, these data are usually encrypted before uploading to the cloud server which impedes data sharing between different medical institutions. Conditional proxy re-encryption (CPRE) allows the proxy to converse ciphertext, especially by specifying a condition embed in the re-encrypt key to achieve fine-grained access control over the ciphertext. Unfortunately, existing CPRE schemes cannot ensure the privacy of the condition, which may contain some sensitive private information. Furthermore, a malicious proxy server may return part of the results and even false data to save its computation or bandwidth. To solve these problems, we propose a blockchain-based condition invisible proxy re-encryption scheme for the e-healthcare system. The proposed scheme guarantees the confidentiality of the data by hiding the condition in the re-encryption key so that the proxy cannot learn anything about the condition. Moreover, the ciphertext-searching algorithm is leveraged by executing the smart contract in the blockchain which ensures the results are correct and complete. Finally, experiment results demonstrate the practicability of the proposed scheme in applications.
An Efficient Approach to Arma Signal Smoothing

Recently, an approach to autoregressive-moving average (ARMA) signal smoothing was introduced that penalizes the variations in the ARMA signal using $\ell_2$-norm.$\ell_2$-norm, however, exhibits weak performance when the model error is distributed by non-Gaussian distribution in particular, when the signal has sharp edges or has discontinuities in boundary points. To overcome this limitation, we present an efficient approach to ARMA signal smoothing that substitutes a sum of absolute values (\ie, $\ell_1$-norm) for the sum of squares (\ie, $\ell_2$-norm) used in the previous ARMA signal smoothing to penalize variations in the ARMA signal.The new ARMA signal smoothing with penalty constraint using $\ell_1$-norm can track the ARMA signal sequences with a more robust tracking behavior against amplitude changes and boundary discontinuities. It is particularly suited for ARMA signals that may have discontinuities at the boundary points or have sharp edges.
Detection, Classification, and Tracking

Automatic image and video exploitation or content analysis is a technique to extract higher-level information from a scene such as objects, behavior, (inter-)actions, environment, or even weather conditions. The relevant information is assumed to be contained in the two-dimensional signal provided in an image (width and height in pixels) or the three-dimensional signal provided in a video (width, height, and time). But also intermediate-level information such as object classes [196], locations [197], or motion [198] can help applications to fulfill certain tasks such as intelligent compression [199], video summarization [200], or video retrieval [201]. Usually, videos with their temporal dimension are a richer source of data compared to single images [202] and thus certain video content can be extracted from videos only such as object motion or object behavior. Often, machine learning or nowadays deep learning techniques are utilized to model prior knowledge about object or scene appearance using labeled training samples [203, 204]. After a learning phase, these models are then applied in real world applications, which is called inference.
Exploring Event-synced Navigation Attacks across User-generated Content Platforms in the Wild

The growth of user-generated content service platforms has led to people relying on user-generated content (UGC) rather than search engines when searching for and accessing information on the web. Attackers can also use UGC on a UGC service platform to disseminate web-based social engineering (SE) attacks to a large number of people. In this paper, we focus on an event-synced navigation attack, a type of web-based SE attack that generates UGC with links to malicious websites and distributes it synced with a real-life event at a specific time. To understand the attacks in the wild, we propose a three-step system to detect event-synced navigation attacks in real time by capturing the inevitable footprints left by attackers. We evaluate each step of the proposed system and determine that the proposed system can classify malicious and non-malicious UGC with 97% accuracy. In addition, we performed a comprehensive measurement study on event-synced navigation attacks spread from popular UGC platforms. We found that 34.1% of the fully qualified domain names of malicious websites associated with the event-synced navigation attack were spread from two or more UGC platforms. Finally, we also found that 87.8% of FQDN associated with well-known type of malicious websites (i.e., information theft, survey scams, suspicious browser plugin installations, etc.) survive for more than 100 days and that countermeasures taken by the UGC platform only covered 31.0% of the malicious UGC we detected in this study even though the malicious websites were accessed frequently.
User Interface Modelling Languages for Normalised Systems: Systematic Literature Review

Normalised System Theory provides a theoretical foundation on how to build software with respect to change over time. An advanced development platform has been built by the NSX company to build Normalised Systems in practice, from modelling tools to implementation. However, there is a lack of support for modelling user interfaces in the platform, so any non-default requirements require manual customisations, which can introduce combinatorial effects and thus harm evolvability. Nevertheless, the research and development of modelling languages for user interfaces has been a continuous effort since the software started using user interfaces. So in this study, we aim to find recent existing UI modelling languages, define the criteria of suitability for modelling UI of Normalised Systems and evaluate them. The results can be used for implementing UI modelling for Normalised Systems.
Exhaustive Search for Weighted Ensemble Classifiers to Improve Performance on Imbalanced Dataset

We compare performance of six single classifiers trained on German credit dataset, an imbalanced dataset of 1000 instances with binary-valued dependent variable. To improve the performance, we consider resampling the dataset and ensembling the classifiers. The benchmarks are taken from the best performance among six considered classifiers. Resampling the dataset includes oversampling and undersampling. The performance of ensemble classifiers are then analyzed and examined. The experimental results provide three benchmarks, i.e. SVM trained on plain dataset, NB trained on plain dataset, and SVM trained on undersampled dataset. Furthermore, ensemble of kNN, LDA and SVM outperforms the first benchmark for all metrics used in this research, i.e. recall 92.71%, precision 79.14%, F1 84.73%, AUC 79.96%, and accuracy 76.88%. The ensemble of LR, SVM and NB and the ensemble of LDA, SVM, and NB outperforms the second and third benchmark, respectively.
Novel Water-Conserving Faucet Attachment

Water wastage can be attributed to people’s lethargy and laxity while using water. Water lost every day by taps and faucets is around 125 million litres in India, critically important to a country that does not have access to clean water for its 163 million inhabitants. Our product reduces water wastage when people utilize taps and faucets for their daily activities and controls the time of water flow during operation for more efficient usage of the limited freshwater resources. It also encourages users to utilize their limited resources effectively and efficiently in a time-bound manner.
Assessment of Digital Competencies in Initial Teacher Training in Chile: What Does the Research Say?

The purpose of this study is to investigate research published on the evaluation of digital competence and digital teaching competence in teacher training in Chile. The objective is to reveal the types of studies, their theoretical conceptual references, what is assessed in them and the main contributions and findings for the strengthening of Initial Teacher Training. From a methodological point of view, a documentary meta-synthesis has been made of the content of research articles published in the last ten years in the WoS, Scopus, and SciELO databases. The search is organized into three categories: Digital Competence, Digital Teaching Competence, and Curriculum Frameworks. Articles are systematized considering the size and context of the participants in the study, purpose and instrument used, theoretical reference, evaluation areas, and results. The main findings of the study show a gap between high levels of Digital Competence and low levels of Digital Teaching Competence among Initial Teacher Training students, and an effort to incorporate these competencies into curricular frameworks.
Kleptographic (algorithmic) backdoors in the RSA key generator

The main types of algorithmic bookmarks are considered. A method for constructing asymmetric kleptographic bookmarks in the RSA key generator is presented, which allows the owner of the bookmark key (the developer or an authorized intelligence agency) to access a user key generated by an infected algorithm. Theorems illustrating the performance of the described algorithms are formulated, and the computational complexity of these algorithms is estimated. The resistance of the built tabs to some classes of attacks is demonstrated even if the adversary knows the methods used and has access to the source code of the key generator.
Circulant Tensor Graph Convolutional Network for Text Classification

Graph convolutional network (GCN) has shown promising performance on the text classification tasks via modeling irregular correlations between word and document. There are multiple correlations within a text graph adjacency matrix, including word-word, word-document, and document-document, so we regard it as heterogeneous. While existing graph convolutional filters are constructed based on homogeneous information diffusion processes, which may not be appropriate to the heterogeneous graph. This paper proposes an expressive and efficient circulant tensor graph convolutional network (CTGCN). Specifically, we model a text graph into a multi-dimension tensor, which characterizes three types of homogeneous correlations separately. CTGCN constructs an expressive and efficient tensor filter based on the t-product operation, which designs a t-linear transformation in the tensor space with a block circulant matrix. Tensor operation t-product effectively extracts high-dimension correlation among heterogeneous feature spaces, which is customarily ignored by other GCN-based methods. Furthermore, we introduce a heterogeneity attention mechanism to obtain more discriminative features. Eventually, we evaluate our proposed CTGCN on five publicly used text classification datasets, extensive experiments demonstrate the effectiveness of the proposed model.