Reviewerly/exHarmony huggingface dataset:
Active Player Detection in Handball Scenes Based on Activity Measures

In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown.
Laplacian Welsch Regularization for Robust Semisupervised Learning

Semisupervised learning (SSL) has been widely used in numerous practical applications where the labeled training examples are inadequate while the unlabeled examples are abundant. Due to the scarcity of labeled examples, the performances of the existing SSL methods are often affected by the outliers in the labeled data, leading to the imperfect trained classifier. To enhance the robustness of SSL methods to the outliers, this article proposes a novel SSL algorithm called Laplacian Welsch regularization (LapWR). Specifically, apart from the conventional Laplacian regularizer, we also introduce a bounded, smooth, and nonconvex Welsch loss which can suppress the adverse effect brought by the labeled outliers. To handle the model nonconvexity caused by the Welsch loss, an iterative half-quadratic (HQ) optimization algorithm is adopted in which each subproblem has an ideal closed-form solution. To handle the large datasets, we further propose an accelerated model by utilizing the Nyström method to reduce the computational complexity of LapWR. Theoretically, the generalization bound of LapWR is derived based on analyzing its Rademacher complexity, which suggests that our proposed algorithm is guaranteed to obtain satisfactory performance. By comparing LapWR with the existing representative SSL algorithms on various benchmark and real-world datasets, we experimentally found that LapWR performs robustly to outliers and is able to consistently achieve the top-level results.
Private Governance of Freedom of Expression on Social Media Platforms: EU content regulation through the lens of human rights standards

Abstract For years, social media platforms have been perceived as a democratic gain, facilitating freedom of expression, easy access to a variety of information, and new means of public participation. At the same time, social media have enabled the dissemination of illegal content and incitement to discrimination, hostility, or violence, fuelling several content regulation initiatives. From the perspective of freedom of expression, this development embraces two challenges: first, private actors govern freedom of expression, without human rights safeguards; second, this privatised governance of human rights is encouraged and legitimised by a broad range of EU policy initiatives. Informed by an analysis of Danish Facebook users’ attitudes toward public debate on Facebook, we pose the question: How do social media companies such as Facebook balance various human rights considerations on their platforms, particularly in relation to freedom of expression? We analyse the abovementioned challenges through a human rights lens, which serves as the analytical framework for this article. Further, we suggest some strategies for moving forward, drawing on recent recommendations from the UN human rights system.
Occlusion-Shared and Feature-Separated Network for Occlusion Relationship Reasoning

Occlusion relationship reasoning demands closed contour to express the object, and orientation of each contour pixel to describe the order relationship between objects. Current CNN-based methods neglect two critical issues of the task: (1) simultaneous existence of the relevance and distinction for the two elements, i.e, occlusion edge and occlusion orientation; and (2) inadequate exploration to the orientation features. For the reasons above, we propose the Occlusion-shared and Feature-separated Network (OFNet). On one hand, considering the relevance between edge and orientation, two sub-networks are designed to share the occlusion cue. On the other hand, the whole network is split into two paths to learn the high semantic features separately. Moreover, a contextual feature for orientation prediction is extracted, which represents the bilateral cue of the foreground and background areas. The bilateral cue is then fused with the occlusion cue to precisely locate the object regions. Finally, a stripe convolution is designed to further aggregate features from surrounding scenes of the occlusion edge. The proposed OFNet remarkably advances the state-of-the-art approaches on PIOD and BSDS ownership dataset.
Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model

We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel. Previously, optimization was only possible through a prior assumption of an explicit simplified channel model.
Preserving Location Privacy in UASN through Collaboration and Semantic Encapsulation

UASN has gained much attention during the past decades. Based on fruitful research and development, UASN could be a compelling component of the global IoT in the future. From the view of location privacy preservation, state-of-the-art investigations mainly concentrate on UASN infrastructures and focus on the location privacy preserving of the source location privacy of the infrastructures, such as anchor nodes, positioning nodes, and communication channel security. However, in the IoT scenario, the underwater positioning service provided by UASN becomes pervasive, and the location privacy of the end-user of this service will come into sight. On the other hand, the presentation model of underwater semantic location is still in use of traditional terrestrial coordinates and geometric points, the concept of underwater semantic location could be further adopted to improve the location privacy. In this article, both these two location privacy issues are concerned. From the view of a UASN positioning service user, we discuss location privacy-related problems and propos a UASN nodes collaboration method to prevent the adversary from learning the precise location of the user when they intercept the communication or hack the central node. Then a concept of "semantic envelope" is designed to encapsulate the location information, which could further strengthen the location privacy.
Enhanced attentive convolutional neural networks for sentence pair modeling

Sentence Pair Modeling is a critical and challenging problem in natural language processing (NLP). It aims to seek the underlying semantic relationship between two sentences. Inspired by human learning, attention mechanisms are widely used in NLP. Previous attentive CNNs mainly focus on attentive pooling which only compute the matching scores between sentence pairs with the same filter size and lack substantive interactive context. In this paper, we propose Enhanced Attentive Convolutional Neural Networks (EACNNs) for modeling sentence pairs to make full use of the characteristics of convolution. Enhanced attention mechanisms help strengthen the interaction between sentences via adding alignment context into local context in convolution operation and combining multi-grained similarity features in different filter sizes. We exploit two attention schemes: (i) attention before representation to capture the interaction information of sentence pairs by attentive convolution (AC) and multi-window advanced attention (MWA), and (ii) attention after representation to emphasize different importance of each word by multi-view similarity measurement layer (MVS). All the enhanced attention mechanisms can make our EACNNs outperform existing attentive CNN models. Furthermore, the combinations of these attention have strong competition in complicated LSTMs and show great advantage in training efficiency.
Linear-time String Indexing and Analysis in Small Space

The field of succinct data structures has flourished over the past 16 years. Starting from the compressed suffix array by Grossi and Vitter (STOC 2000) and the FM-index by Ferragina and Manzini (FOCS 2000), a number of generalizations and applications of string indexes based on the Burrows-Wheeler transform (BWT) have been developed, all taking an amount of space that is close to the input size in bits. In many large-scale applications, the construction of the index and its usage need to be considered as one unit of computation. For example, one can compare two genomes by building a common index for their concatenation and by detecting common substructures by querying the index. Efficient string indexing and analysis in small space lies also at the core of a number of primitives in the data-intensive field of high-throughput DNA sequencing. We report the following advances in string indexing and analysis: We show that the BWT of a string T ∈ {1,…,σ} n can be built in deterministic O ( n ) time using just O ( n log σ) bits of space, where σ ≤ n . Deterministic linear time is achieved by exploiting a new partial rank data structure that supports queries in constant time and that might have independent interest. Within the same time and space budget, we can build an index based on the BWT that allows one to enumerate all the internal nodes of the suffix tree of T . Many fundamental string analysis problems, such as maximal repeats, maximal unique matches, and string kernels, can be mapped to such enumeration and can thus be solved in deterministic O ( n ) time and in O ( n log σ) bits of space from the input string by tailoring the enumeration algorithm to some problem-specific computations. We also show how to build many of the existing indexes based on the BWT, such as the compressed suffix array , the compressed suffix tree , and the bidirectional BWT index , in randomized O ( n ) time and in O ( n log σ) bits of space. The previously fastest construction algorithms for BWT, compressed suffix array and compressed suffix tree, which used O ( n log σ) bits of space, took O ( n log log σ) time for the first two structures and O ( n log ϵ n ) time for the third, where ϵ is any positive constant smaller than one. Alternatively, the BWT could be previously built in linear time if one was willing to spend O ( n log σ log log σ n ) bits of space. Contrary to the state-of-the-art, our bidirectional BWT index supports every operation in constant time per element in its output.
Neural Named Entity Boundary Detection

In this paper, we focus on named entity boundary detection, which is to detect the start and end boundaries of an entity mention in text, without predicting its type. The detected entities are input to entity linking or fine-grained typing systems for semantic enrichment. We propose BdryBot, a recurrent neural network encoder-decoder framework with a pointer network to detect entity boundaries from a given sentence. The encoder considers both character-level representations and word-level embeddings to represent the input words. In this way, BdryBot does not require any hand-crafted features. Because of the pointer network, BdryBot overcomes the problem of variable size output vocabulary and the issue of sparse boundary tags. We conduct two sets of experiments, in-domain detection and cross-domain detection, on six datasets. Our results show that BdryBot achieves state-of-the-art performance against five baselines. In addition, our proposed approach can be further enhanced when incorporating contextualized language embeddings into token representations.
Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

The advent of the wireless communications systems augurs new cutting-edge technologies, including self-driving vehicles, unmanned aerial systems, autonomous robots, the Internet-of-Things, and virtual reality. These technologies require high data rates, ultra-low latency, and high reliability, all of which are promised by the fifth generation of wireless communication systems (5G). Many research groups state that 5G cannot meet its demands without artificial intelligence (A.I.) integration as 5G wireless networks are expected to generate unprecedented traffic giving wireless research designers access to big data that can help in predicting the demands and adjust cell designs to meet the users' requirements. Subsequently, many researchers applied A.I. in many aspects of 5G wireless communication design including radio resource allocation, network management, cyber-security. In this paper, we provide an in-depth review of A.I. for 5G wireless communication systems. in this respect, the aim of this paper is to survey A.I. in 5G wireless communication and networking by discussing many case studies, discuss the challenges, and shed new light on future research directions for leveraging A.I. in 5G wireless communications.
Quantum blockchain using weighted hypergraph states

This paper proposes a protocol to prepare a blockchain using quantum tools which maintains the distributive nature of the blockchain and provides security against a quantum attacker. The authors provide an example of a two blockchain prepared in IBM 5 qubit quantum computer, as a proof of concept with fidelity close to 0.9548.
Machine Learning based System for Automatic Detection of Leukemia Cancer Cell

In recent years, blood cell classification with the help of image processing techniques has attracted many researchers to build an automated system that assists doctors for diagnosis of cancer. Also, it's very challenging to differentiate cancer cell from normal cell as they look similar in initial stages. In this manuscript, we have presented an approach for cancer cell detection by extracting important features from the blood cell images and learning multiple classifiers. We have observed that Gradient Boosting Decision Tree classification algorithms give better result than Support Vector Machine. We have also derived few important features like presence of adjacent nuclei and measure of irregularity in the shape of a nucleus, which has significant impact on cancer cell detection. Our techniques can be used in a limited computing environment without a Graphics Processing Unit. We have achieved 85.6% of F1 score on validation data. This approach also identified an important feature for the images that can help doctors or technicians for better understanding of stained images to aid diagnosis of leukemia patients.
Is Solidity Solid Enough?

We introduce Featherweight Solidity, a calculus formalizing the core features of the Solidity language, thus providing a fundamental step to reason about safety properties of smart contracts' source code. The formalization includes a static type system that represents the foundation of the Solidity compiler. We show that it prevents some errors whereas many others, such as accesses to a non existing function or state variable, are only detected at runtime and cause interruption and rolling-back of transactions. We then propose a refinement of the type system that is retro-compatible with original Solidity code, and statically captures more errors, such as unsafe casts and unsafe call-back expressions.
High-Accuracy Discrimination of Blasts and Earthquakes Using Neural Networks With Multiwindow Spectral Data

Abstract The manual separation of natural earthquakes from mine blasts in data sets recorded by local or regional seismic networks can be a labor-intensive process. An artificial neural network (ANN) applied to automate discriminating earthquakes from quarry and mining blasts in eastern Kentucky suggests that the analyst effort in this task can be significantly reduced. Based on a dataset of 152 local and regional earthquake and 4192 blast recordings over a three-year period in and around eastern Kentucky, ANNs of different configurations were trained and tested on amplitude spectra parameters. The parameters were extracted from different time windows of three-component broadband seismograms to learn the general characteristics of analyst-classified regional earthquake and blast signals. There was little variation in the accuracies and precisions of various models and ANN configurations. The best result used a network with two hidden layers of 256 neurons, trained on an input set of 132 spectral amplitudes and extracted from the P-wave time window and three overlapping time windows from the global maximum amplitude on all three components through the coda. For this configuration and input feature set, 97% of all recordings were accurately classified by our trained model. Furthermore, 96.7% of earthquakes in our data set were correctly classified with mean-event probabilities greater than 0.7. Almost all blasts (98.2%) were correctly classified by mean-event probabilities of at least 0.7. Our technique should greatly reduce the time required for manual inspection of blast recordings. Additionally, our technique circumvents the need for an analyst, or automatic locator, to locate the event ahead of time, a task that is difficult due to the emergent nature of P-wave arrivals induced by delay-fire mine blasts.
Analyzing and documenting the systematic review results of software testing ontologies

Software testing is a complex area since it has a large number of specific methods, processes and strategies, involving a lot of domain concepts. Therefore, it would be valuable to have a conceptualized software testing ontology that explicitly and unambiguously defines the concepts. Consequently, it is important to find out the available evidence in the literature on primary studies for software testing ontologies. In particular, we are looking for research that has a rich ontological coverage that includes Non-Functional Requirements (NFRs) and Functional Requirements (FRs) concepts in conjunction with static and dynamic testing concepts, which can be used in method and process specifications for a family of testing strategies. The main goal for this secondary study is to identify, evaluate and synthesize the available primary studies on conceptualized software testing ontologies. To conduct this study, we use the Systematic Literature Review (SLR) approach, which follows our enhanced SLR process. We set three research questions. Additionally, to quantitatively evaluate the quality of the selected conceptualized ontologies, we designed a NFRs tree and its associated metrics and indicators. We obtained 12 primary studies documenting conceptualized testing ontologies by using three different retrieval methods. In general, we noted that most of them have a lack of NFRs and static testing terminological coverage. Finally, we observe that none of them is directly linked with FRs and NFRs conceptual components. A general benefit of having the suitable software testing ontology is to minimize the current heterogeneity, ambiguity and incompleteness problems in terms, properties and relationships. We have confirmed that exists heterogeneity, ambiguity, and incompleteness for concepts dealing with testing artifacts, roles, activities, and methods. Moreover, we did not find the suitable ontology for our aim since none of the conceptualized ontologies are directly linked with NFRs and FRs components.
A Survey of Key Technologies for High Utility Patterns Mining

Recently, high utility pattern mining (HUPM) is one of the most important research issues in data mining.Because it can consider the non-binary frequency values of items in a transaction and the different profit values of each item.It has been widely used.First of all, this paper briefly describes the related concepts, formulas and examples of application for HUPM.Secondly, the key technologies for HUMP are introduced in detail, and they are divided into main methods including Apriori-based, tree-based, projection-based, list-based, data format-based, and index-based and so on.The paper further compares data sets, uses, advantages and disadvantages of algorithms, laid the foundation for the next research direction.Then, this article outlines the high utility derivative patterns, including high average utility pattern, high utility sequential pattern, and high utility compact pattern and so on.Because static data is difficult to meet the actual needs, this paper summarizes the efficient use of HUPMs' methods over data streams, mainly based on incremental methods, based on the sliding window model methods, based on the time decay model methods and based on the landmark model methods and so on.
BloCyNfo-Share: Blockchain based Cybersecurity Information Sharing with Fine Grained Access Control

To build a proactive cyber defense system, sharing the cybersecurity information has been very popular by which any organization can get more information about unknown and new threats. Cybersecurity Information Exchange (CYBEX) is one of the important platforms which has been playing an important role in implementing proactive cyber defense system by allowing organizations sharing their cybersecurity information. However, they are centralized and therefore they may suffer from complete failure in case of any damage or accident. Moreover, while sharing private information it lacks the mechanism of providing rights to query organizations i.e., enabling the access control over the shared sensitive information. Finally, nonrepudiation of the system does not exist i.e., there is no way to track or keep the record what any organization is sharing and it is necessary to keep the record in case anyone denies after sharing false information. To address these issues, in this paper we propose blockchain based privacy preserving cybersecurity information sharing using proxy re-encryption and attribute-based encryption (BloCyNfo-Share) where the organization can achieve fine-grain access control by delegating which organization can have the access to its cybersecurity information leveraging the benefits of blockchain technology. We conduct privacy and experimental analysis of the proposed system and the findings show that the model is private as well as efficient.