Reviewerly/exHarmony huggingface dataset:
Service Identification of TLS Flows Based on Handshake Analysis

Identification of services constituting traffic from given IP network flows is important for many purposes such as management of quality of service, prevention of security problems, and providing a discounting service for customers only in accessing specified services like zero-rating service. The simplest methods for identifying these services are identifications based on IP addresses and port numbers. However, such methods are not sufficiently accurate and thus require improvement. Deep packet inspection (DPI) is an advanced method for improving the accuracy of identification. Many current IP flows are encrypted with the transport layer security (TLS) protocol. Therefore, an identification method cannot analyze almost all the data encrypted by TLS. In the cases of TLS 1.2 or less, some fields, e.g. server name indication (SNI), in the protocol header for the TLS session establishment are not encrypted and then can be analyzed. Thus, we can expect that the service can be identified from IP flows, which are composed of TLS sessions, by analyzing these fields. For achieving this, two challenges are mainly required. One is grouping TLS sessions by accesses from many TLS sessions that pass through a network element. The other is the identification of service from TLS sessions grouped in the first challenge. In our work, we mainly focus on the second theme, i.e., service identification from given TLS sessions. In our previous work, we proposed a method for identification by analyzing these non-encrypted data based on DPI and n-gram. However, there is room for improvement in identification accuracy because this method analyzed all the non-encrypted data including random values without protocol analysis. In this paper, we propose a new method for identifying the service from given TLS sessions based on SNI with protocol data unit (PDU) analysis. The proposed method clusters TLS sessions according to the value of SNI and identifies services from the occurrences of all groups. We evaluated the proposed method by identifying services on Google, Yahoo, and MSN sites, and the results showed that the proposed method could identify services more accurately than the existing method. The average ratios of inaccurate identifications were decreased by 65%, 72%, and 41% in our experiments of Google, Yahoo, and MSN services, respectively.
Distributed dynamic online learning with differential privacy via path-length measurement

Dynamic online learning has been given great concerns as real-time and non-stationary systems develop and it can be used for solving many practical sequential decision problems like dynamic recommendation systems for online advertisement. Due to the large volume of big data in online learning, dynamic online learning is required to be deployed under the distributed framework where the communication among agents is time-varying and real-time. Meanwhile, since the information sharing among agents is vulnerable to interception by the adversary, the privacy protection problem is significant, but to the best of our knowledge, this work is the first to consider privacy-preserving distributed online learning under dynamic circumstances. Specifically, based on a differentially private distributed online gradient descent algorithm, we minimize the regret of the network. Moreover, via the rigorous mathematical analysis, we achieve the sublinear regret bound under ϵ-differential privacy, that is, we respectively obtain O(T+VT) and O(log⁡T+VT) for convex and strongly convex function, where T is the time horizon and the path-length VT reflects the variation of the minimizer sequence. The experiments performed on the real datasets validate that the presented algorithm achieves acceptable optimization performance even when privacy protection is relatively strict.
Manipulating faces for identity theft via morphing and deepfake: Digital privacy

Digital face images can be easily manipulated for obfuscating or impersonating an identity. Several techniques are used for face manipulation, both traditional computer vision based such as morphing, and modern deep learning based such as deepfake. Morphing and deepfake techniques became advanced enough in creating photorealistic face images. Due to that, these techniques pose a serious threat to identity theft and can significantly harm at a personal level such as the risk of reputation and money, and the national level such as interference in the election. In this chapter, we review (i) different stealthy ways of identity threat generation techniques, (ii) popular databases used in this research direction, and (iii) defense algorithms build to detect these manipulated images. We further provide key open challenges which need to be addressed to make the defense algorithms robust, generalized, and to handle the adaptive nature of the attacks.
To Score or Not to Score: Factors Influencing Performance and Feasibility of Automatic Content Scoring of Text Responses

Abstract In this article, we systematize the factors influencing performance and feasibility of automatic content scoring methods for short text responses. We argue that performance (i.e., how well an automatic system agrees with human judgments) mainly depends on the linguistic variance seen in the responses and that this variance is indirectly influenced by other factors such as target population or input modality. Extending previous work, we distinguish conceptual , realization , and nonconformity variance , which are differentially impacted by the various factors. While conceptual variance relates to different concepts embedded in the text responses, realization variance refers to their diverse manifestation through natural language. Nonconformity variance is added by aberrant response behavior. Furthermore, besides its performance, the feasibility of using an automatic scoring system depends on external factors, such as ethical or computational constraints, which influence whether a system with a given performance is accepted by stakeholders. Our work provides (i) a framework for assessment practitioners to decide a priori whether automatic content scoring can be successfully applied in a given setup as well as (ii) new empirical findings and the integration of empirical findings from the literature on factors that influence automatic systems' performance.
A modular construction of type theories

The lambda-Pi-calculus modulo theory is a logical framework in which many type systems can be expressed as theories. We present such a theory, the theory U, where proofs of several logical systems can be expressed. Moreover, we identify a sub-theory of U corresponding to each of these systems, and prove that, when a proof in U uses only symbols of a sub-theory, then it is a proof in that sub-theory.
Variable selection for quantile autoregressive model: Bayesian methods versus classical methods

In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simulations and data example indicate that the proposed methods are feasible, reliable, and appropriate for analyzing the Bike Sharing data set.
KinRob: An ontology based robot for solving kinematic problems

Intelligent answering technology, which enables computers to solve problems automatically, is often used to develop tutorial systems, and has a wide range of application prospects. However, due to the lack of linguistic analysis and understanding methods, there are few researches on intelligent algorithms for solving kinematics problems. Developing such an algorithm is challenging, because solving kinematics problems is a complex task that includes text understanding, problem analysis, and automatic solution. To understand all these complexities involved in kinematics problems requires background knowledge. And only when an automatic solver contains a powerful internal knowledge representation system can it perform these tasks. We, thus, develop KinRob, an tutorial system for solving kinematics problems by combining neural network and ontology. Firstly, we propose an ontology for KinRob, which defines the knowledge of kinematics, and can help the robot understand a kinematics problem. Secondly, to match the text in natural language with the ontology, we propose a novel tagging scheme based on the kinematic problem understanding model in named entity recognition (NER). Finally, extensive experiments are conducted, and the experimental results show that the performance of the proposed method on a dataset of kinematic problems from authoritative sources better than the baseline algorithms.
Edge detection algorithm in complex image text information extraction

With the rapid development of network technology and information technology, the amount of information contained in images has increased significantly. How to effectively extract text information from complex images has become the focus of current research in this field. Firstly, the Canny algorithm in the edge detection algorithm is improved and applied to the edge detection of complex images. Then the K-means algorithm is optimized to achieve better clustering effect of pixels. Finally, the text information in the image is extracted from the two. The results show that under the influence of salt and pepper noise from 0% to 90%, the quality factor obtained by the improved Canny algorithm is at least 0.4, and the detection accuracy is higher; The minimum peak signal-to-noise ratio of the algorithm is 38, and the maximum mean square error is 30, which are both better than the LOG algorithm and the traditional Canny algorithm, and have better noise reduction effect and image fidelity. It is used together in the extraction process of image text information, and the text recognition accuracy rate of the combined algorithm reaches a maximum of 93%, and is stable at more than 90%, indicating that this method has a high text recognition accuracy rate and provides text extraction for complex images. A reference path is available.
Autonomous gesture recognition using multi-layer LSTM networks and laban movement analysis

In recent years, due to the reasonable price of RGB-D devices, the use of skeletal-based data in the field of human-computer interaction has attracted a lot of attention. Being free from problems such as complex backgrounds as well as changes in light is another reason for the popularity of this type of data. In the existing methods, the use of joint and bone information has had significant results in improving the recognition of human movements and even emotions. However, how to combine these two types of information in the best possible way to define the relationship between joints and bones is a problem that has not yet been solved. In this article, we used the Laban Movement Analysis (LMA) to build a robust descriptor and present a precise description of the connection of the different parts of the body to itself and its surrounding environment while performing a gesture. To do this, in addition to the distances between the hip center and other joints of the body and the changes of the quaternion angles in time, we define the triangles formed by the different parts of the body and calculate their area. We also calculate the area of the single conforming 3-D boundary around all the joints of the body. We use a long short-term memory (LSTM) network to evaluate this descriptor. The proposed algorithm is implemented on five public datasets: NTU RGB+D 120, SYSU 3D HOI, FLORENCE 3D ACTIONS, MSR Action3D and UTKinect-Action3D datasets, and the results are compared with those available in the literature.
Patient Data Analysis with the Quantum Clustering Method

Quantum computing is one of the most promising solutions for solving optimization problems in the healthcare world. Quantum computing development aims to light up the execution of a vast and complex set of algorithmic instructions. For its implementation, the machine learning models are continuously evolving. Hence, the new challenge is to improve the existing complex and critical machine learning training models. Therefore, the healthcare sector is shifting from a classical to a quantum domain to sustain patient-oriented attention to healthcare patrons. This paper presents a hybrid classical-quantum approach for training the unsupervised data models. In order to achieve good performance and optimization of the machine learning algorithms, a quantum k-means (QK-means) clustering problem was deployed on the IBM quantum simulators, i.e.,the IBM QASM simulator. In the first place, the approach was theoretically studied and then implemented to analyze the experimental results. The approach was further tested using small synthetics and cardiovascular datasets on a qsam simulator to obtain the clustering solution. The future direction connecting the dots is the incremental k-means algorithm with the quantum platform, which would open hitherto unimaginable technological doors.
Sequences over Finite Fields Defined by OGS and BN-Pair Decompositions of PSL2(q) Connected to Dickson and Chebyshev Polynomials

The factorization of groups into a Zappa–Szép product, or more generally into a k-fold Zappa–Szép product of its subgroups, is an interesting problem, since it eases the multiplication of two elements in a group and has recently been applied to public-key cryptography. We provide a generalization of the k-fold Zappa–Szép product of cyclic groups, which we call OGS decomposition. It is easy to see that the existence of an OGS decomposition for all the composition factors of a non-abelian group G implies the existence of an OGS for G itself. Since the composition factors of a soluble group are cyclic groups, it has an OGS decomposition. Therefore, the question of the existence of an OGS decomposition is interesting for non-soluble groups. The Jordan–Hölder theorem motivates us to consider the existence of an OGS decomposition for finite simple groups. In 1993, Holt and Rowley showed that PSL2(q) and PSL3(q) can be expressed as a product of cyclic groups. In this paper, we consider an OGS decomposition of PSL2(q) from a different point of view to that of Holt and Rowley. We look at its connection to the BN-pair decomposition of the group. This connection leads to sequences over Fq, which can be defined recursively, with very interesting properties, and are closely connected to Dickson and Chebyshev polynomials. Since every finite simple Lie-type group exhibits BN-pair decomposition, the ideas in this paper might be generalized to further simple Lie-type groups.
Facial feature embedded CycleGAN for VIS–NIR translation

Visible and near-infrared (VIS–NIR) heterogeneous face recognition remains a challenging task due to distinctions between spectral components of two modalities and insufficiently pairwise VIS–NIR data. Inspired by the cycle-consistent generative adversarial network (CycleGAN), this paper proposes a facial feature embedded CycleGAN to translate between VIS and NIR face images, aiming to enable the distributions of translated (fake) images to be similar as those of true images. To learn the particular feature of NIR or VIS domain while preserving common facial representation between VIS and NIR domains, a facial feature extractor (FFE), tailored specifically for extracting effective feature from face images, is embedded in the generator of original CycleGAN. For implementing the FFE, we use the MobileFaceNet which is pre-trained on a VIS face database. The domain-invariant feature learning is enhanced by proposing a new pixel consistency loss. Additionally, we establish a new WHU VIS–NIR database including varies in face rotation and expressions to enrich the insufficient training data. Moreover, experiments on the well-known Oulu-CASIA NIR–VIS database and our WHU VIS–NIR database validate the potential benefit of the proposed FFE-based CycleGAN (FFE-CycleGAN). In particular, we achieve 96.5% accuracy on Oulu-CASIA and 98.9% accuracy on WHU VIS–NIR.
Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives

Digital archives (DAs) usually store diverse expert-level materials. Nowadays, access to DAs is increasing for non-expert users, However, they might have difficulties formulating appropriate search queries to find the necessary information. In response to this problem, we propose a query log-based query recommendation algorithm that provides expert knowledge to non-expert users, thus supporting their information seeking in DAs. The use case considered is one where after users enter some general queries, they will be recommended semantically similar expert-level queries in the query logs. The proposed modified conditional restricted Boltzmann machines (M-CRBMs) are capable of utilizing the rich metadata in DAs, thereby alleviating the sparsity problem that conventional restricted Boltzmann machines (RBMs) will face. Additionally, compared with other CRBM models, we drop a large number of model weights. In the experiments, the M-CRBMs outperform the conventional RBMs when using appropriate metadata, and we find that the recommendation results are relevant to the metadata fields that are used in M-CRBMs. Through experiments on the Europeana dataset, we also demonstrate the versatility and scalability of our proposed model.
An Introduction to Data Visualization

In the era of information explosion, users are exposed to overwhelming visual stimuli daily. Unprocessed raw data are difficult to understand by the human brain. The business field has more data from more references than ever before. However, the information being presented to most employees, much of the time, is displayed in the form of complicated spreadsheets and hackneyed charts that are missing the crucial insights that lends itself to better business understanding. Thankfully, human beings are very sensitive to the perception of graphics and images; therefore, the imagery behind the numbers in the data graphics makes it easier for people to understand. Data visualization is becoming the best way to communicate with people. The present paper will explore the importance of data visualization and the current uses of data visualization. It will also examine the different tools and potential problems, and a brief on data visualization is included.
How secure is your organisation?

The threats facing organisations go beyond the cyber realm. We're heading into a tough economic climate and organisations must overcome a range of challenges. This could lead to cyber security slipping down the list of priorities. But this is no time to drop your guard. The fact is, in this environment you can't afford a cyber security incident.
Non-Local Means De-Speckling Based on Multi-Directional Local Plane Inclination Angle

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified.
Folding left and right matters: Direct style, accumulators, and continuations

Abstract The equivalence of folding left and right over Peano numbers and lists makes it possible to minimalistically inter-derive (1) structurally recursive functions in direct style, (2) structurally tail-recursive functions that use an accumulator, and (3) structurally tail-recursive functions in delimited continuation-passing style, using Ohori and Sasano’s lightweight fusion by fixed-point promotion. When the fold-left and the fold-right functions account for primitive iteration for Peano numbers, this equivalence is unconditional. When they account for primitive recursion for Peano numbers, this equivalence is modulo left permutativity of their induction-step parameter – a property which is more general than associativity and commutativity. And when they account for primitive iteration or for primitive recursion over lists, this equivalence is modulo left permutativity of their induction-step parameter if these two fold functions have the same type. Since the 1980s, however, the two fold functions for lists do not have the same type: the arguments for their induction-step parameter are swapped, a re-ordering that complicated Bird and Wadler’s duality theorems and whose history is reviewed in an appendix. Without this re-ordering, Bird and Wadler’s second duality theorem more visibly accounts for “re-bracketing,” which is a key step to make recursive programs tail recursive in the general area of program development, from Cooper in the 1960s and onwards.
The Role of Cyber Notaries in Supporting the Tourism Investment Climate in Bali

Bali has abundant natural beauty, culture, and customs, attracting investors to invest in Bali.The investment in Bali that investors most target is property.The increasing number of transactions on property in Bali also requires a form of legal binding (agreement) based on the urge of the buyer or investor with the seller or property owner.Some officials are given the authority to enact documentary evidence for legal events named the Notary.The concept of a cyber Notary in Indonesia is still under debate.Although technology allows the role of Notaries online and remotely, legally, it seems that it cannot be carried out quickly.A statutory method was used in this normative legal research.It is examined using the description, systematization, and explanation phases.In Indonesia, the legislation relating to cyber Notaries is established in Article 15, paragraph 3 of the Notary Profession Law, which mentions a glimpse of the cyber Notary in terms of the competence to certify transactions undertaken online.However, in light of the present rapid growth of technology, this authority remains modest and clear.The role of cyber notaries in supporting investment in the tourism sector in Bali through the implementation of making engagements and/or making authentic deeds carried out through technological assistance will certainly help and facilitate investors in conducting transactions, especially in the tourism sector.
A Systematic Study of Krill Herd and FOX Algorithms

In 2012