Reviewerly/exHarmony huggingface dataset:
Complex Pairwise Activity Analysis Via Instance Level Evolution Reasoning

Video activity analysis systems are often trained on large datasets. Activities and events in the real world do not occur in isolation, instead, they occur as interactions between related objects. This work introduces a novel method that jointly exploits relational information between pairs of objects and temporal dynamics of each object. The proposed method effectively leverages a new simple architecture that is flexible and easily trained to detect relational activities and events using small datasets (hundreds of samples). The solution is constructed and tested using synthetic videos of car-collision events. The annotated datasets in this work will be made available online to the research community. Experimental results demonstrate the efficacy of the network to perform complex activity analysis.
Attack-Defense Utility Quantification And Security Risk Assessment

With the developing of the attack and defense technology, the cyber environment has been more and more sophisticated. We failed to give an accurate evaluation of network security situation, as we lack a quantitative assessment of attack-defense behaviors. In response to this situation, we extended the attack-defense stochastic game model (ADSGM) in terms of the utility calculation to evaluate attack-defense behaviors more accurately. Moreover, we analyzed the different defensive capabilities of distinct defense mechanism, putting forward a corresponding utility calculation coping with these capabilities. Through a case study, we showed the impact of defense capabilities as well as the attack time we defined, demonstrating the effectiveness of our methods on attack-defense behavior quantification. This paper filled up the gap in the quantitative assessment of defensive measures, which makes the quantitative evaluation of attack-defense more comprehensive and accurate.
Application of U-Net with Filter in Rectal Tumor Recognition

U-NET is a full convolution network composed of convolution and upsampling. It is widely used in medical image recognition and segmentation. The basic idea of U-NET is to transform image segmentation into classification. The core problem of U-NET is still to solve the optimal parameters of loss function so as to facilitate the subsequent reverse propagation steps. But when the loss function is not convex, the problem often becomes very complex. The main purpose of this paper is to use U-NET neural network model with filter to solve the problem of accurate recognition and segmentation of tumor regions. By introducing filter theory, some theoretical defects of traditional algorithm can be avoided, and the problem of effective division of rectal tumor region can be solved.
A Classification Model Based on Improved Self-Adaptive Fireworks Algorithm

As a recently developed swarm intelligence algorithm, fireworks algorithm (FWA) is an optimization algorithm with good convergence and extensible properties. Moreover, it is usually able to find the global solutions. The advantages of FWA are both optimization accuracy and convergence speed which endue the FWA with a promising prospect of application and extension. This chapter mainly focuses on the application of FWA in classification problems and the improvement of FWA. Many prior studies around FWA have been produced. The author here probes improvement of FWA and its application in classification. The chapter studies FWA around: (1) Application of FWA in classification problems; (2) Improvement of FWA's candidate solution generation strategy (CSGS), including the employment of self-adaptive mechanisms; (3) Improved SaFWA and classification model. For each part, the author conducts research through theory, experimentation, and results analysis.
Drift Detection and Correction Post-Tracking

Accurate object tracking is a challenging problem due to numerous factors, that may cause the tracker to drift away from the target object. Typically, the output of a tracker is a bounding box (BB); such BB may not well discriminate the object from its background and may not be centered correctly around the object. This paper proposes a method that first detects, at each frame, if a tracker tends to drift by analyzing saliency features of the output BB of a tracker, and then applies automatic seeded object segmentation on the BB to correct the drift once detected. Such segmentation is meant to relocate (recenter) the BB adaptive to the object segmented. As seeds, we propose to use SIFT and salient points conditioned they are non-background pixels. Different than related work, our approach thus models drift external to a base tracker by examining its output BB at each and corrects drift, as needed, by updating that BB adaptive to segmentation. We show the ability of the proposed method to significantly improve the tracking quality of base trackers. We also show that the proposed method outperforms by far segmentation-based trackers.
Improved Integral Analysis on Lightweight Block Cipher Midori

As a dual cryptanalysis corresponding to differential analysis, integral cryptanalysis is more effective than differential and linear analyses in analyzing of some structures and algorithms. A new integral distinguisher for Midori is proposed in this paper by using saturating technology and combining the characteristics of the linear diffusion layer of Midori. The number of active bytes taken by our integral distinguisher is less than the number of active bytes of the distinguisher constructed by automatically searching. Furthermore, it is attacked parity bytes of key combining with the characteristic of the key schedule. To sum up, the integral analysis in this paper is the optimal integral analysis at present.
Search for methodological benchmarks in humanitarian studies in the context of artistic pedagogy

The article states that post-neoclassical rationalism, which is approved, not only significantly expands, but also complicates the worldview of a modern person, changes his outlook positions, causing the need to develop new methodological approaches and value-purpose objects in scientific knowledge. Today there was a need to rethink the issue of methodological benchmarks in the humanities research, which is actualized by the leading role of humanitarian knowledge in the socio-cultural development of modern society. To study this issue, the following research methods are used: analytical-synthetic, inductive-deductive and generalized, and modelling ones. On the basis of scientific reflection, the essence of methodological approaches in the humanities research, which are diversified and acquire new meaningful characteristics in the conditions of changing the paradigm of humanitarian knowledge, is considered. The theoretical and methodological positions in relation to the value dominant in humanitarian knowledge, which is oriented towards subjectivity and existentialism and provides in-depth reflection on artistic phenomena, are singled out. It is noted that in the humanitarian methodology the following research methods are found: ideographic method (research of individuals, events), methods of hermeneutics (interpretation of meaningful personal actions), qualitative analysis (understanding of the phenomenon, and not its calculation), method of phenomenology (knowledge of the causes and essence of artistic educational phenomena), methods of self-understanding, allowing to understand subjectity and spirituality of a person, humanitarian expertise, humanitarian design, existential dialogues, etc. The article outlines promising vectors of the epistemological environment of artistic pedagogy, which serve as the basis for developing new scientific strategies at the methodological, theoretical and methodical levels.
HyperTube: A Framework for Population-Based Online Hyperparameter Optimization with Resource Constraints

Online data streaming has become one of the most common data forms in the modern world, which imposes a strong demand for developing hyperparameter optimization techniques for online learning algorithms. In fact, processing online streaming data can be considered as a constraint on the training process. Existing studies do not provide a clear framework for modeling appropriate constraints with hyperparameter optimization in this context. In this paper, we propose a framework, called HyperTube, based on a set of assumptions that clearly define the constraints and objective function for online hyperparameter optimization under limited computing resources. We also introduce a “micro-mini-batch training mechanism” to reuse online data mini-batches in an efficient manner. Numerical experiments compare the performances of different training settings under the constraints of HyperTube. The results on stationary data streams without concept drift indicate that training on incremental data samples with model selection efficiently uses computing power and gives satisfactory validation performance compared with training without model selection. Meanwhile, the results on data streams with significant concept drift indicate that parallel updating could lead to relatively good model performance. In both cases, with the best settings, HyperTube with the micro-mini-batch training mechanism significantly outperforms offline random search with the same amount of computational resources. These settings can be further improved by a modified genetic algorithm. Finally, we develop a systematic method for selecting suitable settings based on a set of criteria.
Weakly-Supervised Semantic Segmentation via Self-training

Abstract Weakly-supervised semantic segmentation with image tags is a challenging computer vision task. Unlike pixel-level masks, image tags give high level semantic information, without low level appearance information. In this paper, we propose an iteratively self-training framework to bridge this two information, which expand and refine the pseudo-labels with training process going. Initial masks are generated from classification network. In the top-down step, rendered images and its labels as well as spatially weight loss are added to jointly training the model for alleviate the effect of inaccurate object masks. Then in the bottom-up step, an adaptive threshold to the confidence model predictions to keep predicted masks reliable. The top-down and bottom-up steps are conducted iteratively to extract the fine object mask. Experiments on our self-build dataset and GTA5 to CityScapes demonstrate the effectiveness of proposed framework.
Unsupervised Outlier detection algorithm based on k-NN and fuzzy logic

Given a set of observations an outlier is a measurement that differs significantly from other observations. In a real application, what is sought is to eliminate them since their processing implies statistical errors. Although there are several works that have addressed the outlier detection challenge, in recent works, efforts have been focused to unsupervised scenario because it does not require any a priori knowledge of data distributions and is more attached to reality. Unfortunately, unsupervised approaches have limitations under complex datasets. In order to solve this problem, we propose the use of K-NN rule and fuzzy logic for outlier detection. First, the proposed algorithm is evaluated by using synthetic data; after, the Harvard Unsupervised Anomaly Detection Benchmark Dataset, which consists of several complex data structures based in real-world applications, is used. In comparison with the current works, our algorithm outperforms most previous works for the Harvard Breast cancer dataset dataset (ROC score equal to 0.9980) while for the Harvard Pen Global dataset our algorithm achieves relatively higher accuracy (more accurate than some previous works) and similar results than most accurate algorithms in the current literature.
A Method of Chinese Tourism Named Entity Recognition Based on BBLC Model

In recent years, the booming development of tourism has accumulated a lot of tourism data. The tourism named entity recognition (NER) is of great significance for efficiently obtaining effective information and providing highquality services. However, in terms of data sets, the existing SIGHAN 2006 bakeoff-3 annotation set only contains the people names, location names and organization names, and lacks other types of entity tags. So, this paper embarks from the characteristics of tourism, a certain amount of tourism text was crawled from Ctrip and annotated five types of entities including people name, location name, organization name, time and things with BIO tag rule. On this basis, the BBLC (BERTBiLSTM-CRF) model is constructed which using Google's latest natural language processing model BERT. Compared with BiLSTM-CRF and CRF, the method shows that the method has higher precision, recall rate and F value when performing named entity recognition of tourism data.
Private resource allocators and their applications

This paper introduces a new cryptographic primitive called a private resource allocator (PRA) that can be used to allocate resources (e.g., network bandwidth, CPUs) to a set of clients without revealing to the clients whether any other clients received resources. We give several constructions of PRAs that provide guarantees ranging from information-theoretic to differential privacy. PRAs are useful in preventing a new class of attacks that we call allocation-based side-channel attacks. These attacks can be used, for example, to break the privacy guarantees of anonymous messaging systems that were designed specifically to defend against side-channel and traffic analysis attacks. Our implementation of PRAs in Alpenhorn, which is a recent anonymous messaging system, shows that PRAs increase the network resources required to start a conversation by up to 16× (can be made as low as 4× in some cases), but add no overhead once the conversation has been established.
Cross Image Cubic Interpolator for Spatially Varying Exposures

Spatially varying exposures via rolling shutter is an efficient way to capture differently exposed images for high dynamic range (HDR) scenes. Neither camera movement nor moving objects is an issue for such a captured method. However, a possible issue is that the resolution of captured images is reduced. In this paper, we introduce a novel cross image cubic interpolator for the spatially varying exposures via the rolling shutter. Both intra correlation among pixels with the same exposure and inter correlation among pixels with the different exposures are utilized by the proposed interpolator. Experimental results show that quality of upsampled images is significantly improved.
Factors Influencing Information Security Policy Compliance Behavior

Globally, banks are deploying reliant data protection technologies to secure business continuity. However, information security attackers leverage on employees seldom predisposition to compromise firms' assets. The study therefore examines attributing factors influencing information security compliance behavior in the Ghanaian banking sector. A survey design approach was adopted to validate the research model. Partial Least Square Structural Equation Modeling (PLS-SEM) were used to analyze 329 valid data. The model results showed that perceived threat, vulnerability, response cost, and efficiency had a significant effect on compliance but interestingly not for Self-Efficacy. Perceived threat severity had the most significant effect on IS compliance behavior. Overall, our model accounted for 60% of the variation in IS compliance.
Advanced Isomap Based on Data Nuggets Algorithm

Manifold learning is a recent popular approach to find the internal low-dimensional structure from the high-dimensional nonlinear data sets embedded in the European space. It is based on the idea that the data we can observe are actually mapped from a low-dimensional space to a high-dimensional one. Given the limitation of the internal characteristics of the data, those artificial high-dimensional data produce dimensional redundancy. In fact, these data can be represented in the lower-dimension space. Algorithms for this task are to achieve efficient and accurate data analysis results. After studying some common algorithms purposed for manifold learning, we find that there is still an improvement for their efficiency. To be specific, in order to reduce computational complexity of the Isometrical Mapping, we introduce a new solution through the concept of data nuggets” to do data partition and selection. This concept is to reduce a large dataset into a smaller set of nuggets of data, each of which contains a center, weight and a scale parameter. While the data is re-expressed as data nuggets, we can apply algorithms for Isomap. This combination can largely reduce the computational complexity due to the decreased dataset. Meanwhile, it still maintains the advantages of Isomap that keep the geodesic distances between the points unchanged.
Internal state recovery of Espresso stream cipher using conditional sampling resistance and TMDTO attack

<p style='text-indent:20px;'>Espresso is a stream cipher proposed for the 5G wireless communication system. Since the design of this cipher is based on the Galois configuration of NLFSR, the cipher has a short propagation delay, and it is the fastest among the ciphers below 1500 GE, including Grain-128 and Trivium. The time-memory-data tradeoff (TMDTO) attack on this cipher and finding the conditional BSW sampling resistance are difficult due to its Galois configuration. This paper demonstrates the calculation of conditional BSW-sampling resistance of Espresso stream cipher, which is based on Galois configuration, and also mounts the TMDTO attack on the cipher by employing the calculated sampling resistance. It is also shown that the attack complexities of TMDTO attack are lower than those claimed by the designers of the ciphers.
Design of Multi-channel CMUT Phase-controlled Transmission System Based on ZYNQ-7010

A multi-channel ultrasonic phase-controlled transmitting system based on ZYNQ-7010 is proposed. ARM (PS core) and FPGA (PL core) are embedded into ZYNQ and they are connected through AXI bus. The PLL in the FPGA is able to change the phase of the system clock, which makes the system have a high resolution of pulse delay. The proposed system has the advantages of simple structure, low cost, and high accuracy. Therefore, it benefits biomedical image processing. We introduce the transmission mechanism of CMUT phase-controlled array and propose the design scheme of multi-channel ultrasonic phase-controlled array transmitting system. The system is implemented in details using ZYNQ-7010. Finally, the proposed phase-controlled array transmitting system is verified in the tests. The minimum delay resolution is obtained to be 2.6 (ns).
Finding Behavioural Patterns Among League of Legends Players Through Hidden Markov Models

This work is aimed at finding behavioural patterns among professional players of League of Legends, one of the greatest recent phenomena in the world of video games. For that purpose, Hidden Markov Models (HMM) are used to model the sequence of events produced by a gameplay. First, the set of interesting game events for analysis is defined, and based on that, each gameplay of the dataset is transformed into a sequences of events. Then, four HMMs will be trained with the data from four different groups of sequences, according to the team that produces the events of the sequence (red/blue) and to whether that sequence led to a victory of the team or not. Finally, the resulting HMMs will be visualized and compared in order to achieve some conclusions about the macro game strategy in League of Legends, which will help to understand the game at the highest level of its competition.
Statistical matching techniques in order to plan interventions on socioeconomic weakness: An Italian case

The techniques for integrating data from multiple sources have the objective of identifying records relating to similar or equal units. Moreover, they estimate the joint distribution of several variables observed on different data files and merge information records. As an example, the estimation of the family risk of poverty, based on “objective” indicators (such as income or recourse to indebtedness), is independent from the awareness of those directly involved and therefore is correctly used in order to plan effective interventions against poverty. However, the researchers could usefully investigate also on the “subjective” perceptions of the standard of living and the causes that lead to socioeconomic hardships of families, but often such perceptions are referred to sub-samples of the previous population (or to different samples of it). In this work, we describe a useful model to integrate data between two surveys (Eurostat EU-SILC and Lifestyles survey) through a Statistical Matching method (hot deck distance). The Cluster Analysis is an essential component of the chosen technique of data integration, as well as of the following descriptive analysis.
A Vulnerability Detection Model for Java Systems Based on Complex Networks

With the deepening of research on network theory, scientists have gradually begun to adopt complex networks to look at complex systems in nature. A complex network is a network of a large number of closely related and interacting units. This paper believes that each component of the software can be abstracted into various algebraic components. These algebraic components are assembled into a software system. Its assembly process satisfies the configuration and fit characteristics in the topological space. Using algebraic components as the vertices of complex network graphs, the connection relationship between them is taken as the edge of the complex network graph. Using this new perspective, the software system is regarded as a complex network graph composed of algebraic components and their connection relationships. We have proved that the software system can be abstracted into the form of algebraic component expressions, and can find algebraic components that may have vulnerabilities based on the eigenvalues of complex networks.
Relativism Digital Forensics Investigations Model: A Case for the Emerging Economies

Digital forensic investigations (DFI) is a process of investigating computers and its associated media to determine whether it has been used to commit a crime or gain unauthorized access. cyberattacks and cybercrimes can be committed globally but reported locally. However, DFI processes vary relative to a particular jurisdiction. Relativism is the perception of universal norms of what is right and wrong or legal and illegal. Although cybercrimes are illegal, what constitutes illegal is relative to a jurisdiction. Cyber espionage attacks may be considered legal or illegal based on economic advantage for someone or as target for attack based on motive and intent. Further, following legal procedures in evidence gathering at a digital crime scene is critical for prosecution. However, there are challenges in gathering evidence using the existing DFI models on all attacks. UNODC, report on the globalization of cybercrimes highlighted the challenges of cybercrime and ranked some emerging economies among the first 10 offending nations globally. There are existing models that are specific to certain jurisdictions and assist the judiciary, law enforcement agencies, and forensic experts. Consequently, presenting digital forensic evidence in court has proved to be challenging, due to a lack of procedures and DFI models specific to emerging economies. In this paper, we identify the phase that is relevant and could facilitate DFI processes from emerging economies' perspective. Further, we review some existing models to determine their relative procedures. This paper does not negate existing models, rather derives a relative model from existing models. We propose a model that will improve the DFI process from the result of the evaluation with inference from international standards.