Reviewerly/exHarmony huggingface dataset:
Finding Fast Flux Traffic in DNS Haystack

Fast-Flux (FF), a technique to associate hostname to multiple IP addresses, has been used by cybercriminals to hide their botnet server responsible for its anonymity and resiliency. The operation FF network service, often used for a phishing campaign and propagate malware to attack critical infrastructure, is quite similar to the operation of the Content Delivery Network (CDN) service, making it more challenging differentiating between the two services. In this research, the authors present a case study of how FF operate and can be detected in Internet Service Provider (ISP) network infrastructure, a high volume of DNS traffic was collected over the five months and analyzed by extracting several DNS features and feed into K-means clustering to distinguish between these two services. During the experiment, the authors show that utilizing web service content as one of the elements can differentiate between the two services with a purity value of 0.922.
Enclavisor: A Hardware-software Co-design for Enclaves on Untrusted Cloud

The releases of Intel SGX and AMD SEV mark the transition of hardware-based enclaves from research prototypes to mainstream products. These two paradigms of secure enclaves are attractive to both the cloud providers and tenants, since security is one of the key pillars of cloud computing. However, it is found that current hardware-defined enclaves are not flexible and efficient enough for the cloud. For example, although SGX can provide strong memory protection with both confidentiality and integrity, the size of secure memory is tightly restricted. On the contrary, SEV enables enclaves to use more memory but has critical security flaws due to no memory integrity protection. Meanwhile, both types of enclaves have relatively long booting latency, which makes them not suitable for short-term tasks like serverless workloads. After an in-depth analysis, we find that there are some intrinsic tradeoffs between security and performance due to the limitation of architectural designs. In this article, we investigate a novel hardware-software co-design of enclaves to meet the requirements of cloud by placing a part of the logic of the enclave mechanism into a lightweight software layer, named Enclavisor, to achieve a balance between security, performance, and flexibility. Specifically, our implementation is based on AMD's SEV and, Enclavisor is placed in the guest kernel mode of SEV's secure virtual machines. Enclavisor inherently supports memory encryption with no memory limitation and also achieves efficient booting, multiple enclave granularities, and post-launch remote attestation. Meanwhile, we also propose hardware/software solutions to mitigate the security flaws caused by the lack of memory integrity. We implement a prototype of Enclavisor on an AMD SEV server. The experiments on both micro-benchmarks and application benchmarks show that enclaves on Enclavisor can have close-to-native performance.
Input Image-Based Dictionary Formation in Super-Resolution for Online Image Streaming

Real-time images are very much desirable in different real-life applications such as defense, medical, satellite, tv, Internet. Construction of relevant dictionary for the training of computer during the prediction of high-resolution image from low-resolution images in real-time requires high mathematical computations. This paper presents a new way of dictionary formation for learning-based super-resolution of real-time streaming of images. Here, the dictionary is formed with help of images that are similar to the input image in terms of structural similarity score. It helps in reducing memory requirement for the dictionary formation. Further, to speed up the process, a technique based on similarity score is proposed for updating of the dictionary. This involves the comparison of current image in the input sequence with the present reference image that was used for dictionary formation. Efficacy of the algorithm is shown through extensive simulations.
Seeds Optimization for Entity Alignment in Knowledge Graph Embedding

The embedding-based entity alignment method usually uses pre-aligned entities as seed data, and aligns the entities in different knowledge graphs through seed entity constraints. This method relies heavily on the quality and quantity of seed entities. In this paper, we use an algorithm to optimize the selection of seed entities, and select seed entity pairs through the centrality and differentiability of entities in the knowledge graph, in order to solve the problem of insufficient number of high-quality seed entities, an iterative entity alignment method is adopted. We have done experiments on dataset DBP15K, and the experimental results show that the proposed method can achieve good entity alignment even under weak supervision.
Gluing and Cutting Cube Tiling Codes in Dimension Six

Let S be a set of arbitrary objects, and let $$s\mapsto s'$$ be a permutation of S such that $$s''=(s')'=s$$ and $$s'\ne s$$ . Let $$S^d=\{v_1\dots v_d:v_i\in S\}$$ . Two words $$v,w\in S^d$$ are dichotomous if $$v_i=w'_i$$ for some $$i\in [d]=\{1,\dots ,d\}$$ , and they form a twin pair if $$v_i'=w_i$$ and $$v_j=w_j$$ for every $$j\in [d]\setminus \{i\}$$ . A polybox code is a set $$V\subset S^d$$ in which every two distinct words are dichotomous. A polybox code V is a cube tiling code if $$|V|=2^d$$ . A 2-periodic cube tiling of $$\mathbb {R}^d$$ and a cube tiling of flat torus $$\mathbb {T}^d$$ can be encoded in a form of a cube tiling code. A twin pair v, w in which $$v_i=w_i'$$ is glue (at the ith position) if the pair v, w is replaced by one word u such that $$u_j=v_j=w_j$$ for every $$j\in [d]\setminus \{i\}$$ and $$u_i=*$$ , where $$*\notin S$$ is some extra fixed symbol. A word u with $$u_i=*$$ is cut (at the ith position) if u is replaced by a twin pair q, t such that $$q_i=t_i'\ne *$$ and $$u_j=q_j=t_j$$ for every $$j\in [d]\setminus \{i\}$$ . If $$V,W\subset S^d$$ are two cube tiling codes and there is a sequence of twin pairs which can be interchangeably gluing and cutting in a way which allows us to pass from V to W, then we say that W is obtained from V by gluing and cutting. In the paper it is shown that for every two cube tiling codes in dimension six one can be obtained from the other by gluing and cutting.
SCF <sup>MSP</sup>

Information leakage through side-channels poses a serious threat to the security of distributed systems. Recent research on countermeasures against side-channel attacks show that, on embedded platforms with predictable execution times, certain classes of these vulnerabilities can be detected and mitigated automatically by means of language-based security techniques. In this paper, we propose a security type system to statically analyse MSP430 assembly programs to detecting information leakage through novel interrupt-latency attacks (a.k.a. Nemesis), timing side-channels, and undesired information flow. We have implemented our technique in a tool, Side Channel FinderMSP, which automatically verifies MSP430 object-code programs to be free of such vulnerabilities. We evaluate the effectiveness of our tool by applying it to a representative set of vulnerable and benign programs. Our experiments demonstrate that the tool is both effective in detecting vulnerabilities, and scalable to realistic applications.
Distorted Probabilities and Bayesian Confirmation Measures

Bayesian Confirmation Measures (BCMs) assess the impact of the occurrence of one event on the credibility of another. Many measures of this kind have been defined in literature. We want to analyze how these measures change when the probabilities involved in their computation are distorted. Composing distortions and BCMs we define a set of Distorted Bayesian Confirmation Measures (DBCMs); we study the properties that DBCMs may inherit from BCMs, and propose a way to measure the degree of distortion of a DBCM with respect to a corresponding BCM.
Word Level Domain-Diversity Attention Based LSTM Model for Sentiment Classification

Sentiment classification is an important task in Natural Language Processing research and it has considerable application significance. The complexity of human sentimental opinion implies that the hidden information such as application scenes or domains that behind the text may play an important role in the prediction of sentiment polarity. This paper presents a novel model for Sentiment Classification, Domain-Diversity Attention Mechanism based LSTM Model (DDAM-LSTM), integrating word level domain relevant features into an input side attention mechanism of LSTM model. Firstly, we propose a representing and calculating method of domain relevant features for each word according to its context. Then we find that the common words and certain domain-specific words show obvious different distribution states as for domain tendency. On this basis, an attention mechanism is designed to assign scale weights to the words at the input side of LSTM network according to their diversity of domain tendency. By combining this unique attention mechanism with the LSTM model, we achieve the goal of fusing the implied domain knowledge with the Neural Network. Experimental results on three public benchmark datasets show that our proposed model yields obvious performance improvement.
Fidelity of Finite Length Quantum Codes in Qubit Erasure Channel

We consider the probability of decoding error, equivalently the fidelity, of finite length quantum codes assuming quantum information transmission via qubit erasure channel. First, we obtain an analytical expression on this probability via combinatorial invariants of quantum codes. Next, we derive a generalization of the MacWilliams identities for those invariants and use them to formulate a linear programming optimization problem, which allows us to obtain lower bounds on the number of unrecoverable sets of i erasures and the probability of decoding error. Our example shows that the obtain bounds can be very tight. Next, we derive upper (achievability) bounds on the probability of decoding error for finite length stabilizer and CSS codes. CSS codes have many advantages for practical applications. Our results show that CSS codes of small lengths, like 100 qubits, visibly lose in performance to generic stabilizer codes.
A Knowledge Graph Embedding Method Based on Neural Network

As the basis of many knowledge graph completion tasks, the embedding representation of entities and relations in knowledge graph (KG) is an important task in the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI). While most of the existing knowledge graph embedding (KGE) models based on convolutional neural network (CNN) can obtain abundant feature embedding, they may ignore an important fact that the triples in the KG come from the text, as they simply learn about the feature embedding of entities and relations without considering contextual information. Therefore, in this paper, we propose an effective KGE model based on neural network. First of all, we convert the triple (h,r,t) of the KG into a sentence [h r t]. Then, the LSTM neural network is used to learn the long-term dependence of sentences from the input feature vectors. Then, on this basis, the two-layer convolutional neural network with several different filters is used to extract different local features. Finally, the obtained feature vectors are connected together, and the inner product is carried out with the weight vectors to obtain the score of the triple, so as to judge the validity of the given triple. We evaluate our model on two benchmark datasets FB15k-237 and WN18RR, the experimental results show that the model can effectively improve the accuracy of link prediction, achieving better results compared with other baseline models.
Towards Digital University

The article is devoted to transformation of traditional university into digital one. The goal of the research was to determine the main directions and develop recommendations of the transformation of the university based on the theoretical principles of project and change management, the experience of international and domestic universities and original research in the field of digitalization. Findings of desk and field research were implemented to the object of study International Information Technologies University located in Almaty, Kazakhstan.
Cryptanalysis of the Modified SHA256

Different security algorithms are applied to produce hashes or ciphertexts to make the data protected from any attack. To determine that these algorithms hold capabilities in security data, cryptanalysis must be performed to determine its resistance to any attack. Cryptanalysis is the process of exploring to break the code used to hash or encrypt data. In this paper, the researchers proved that the modified SHA256 is viable to length extension, brute-force and dictionary attacks. Randomness tests also showed uniform random distribution of the hashes generated by the modified SHA256.
Extraction of structured data from unstructured medical records using text data mining technologies: process automation

Abstract The paper discusses technologies for processing text-based medical data stored in the Microsoft Word text format. Processing such data is aimed at data mining the text for new, potentially useful knowledge that can later be used to study various diseases and to form a personalized approach to diagnosis and treatment. During the study, 3244 depersonalized medical records of children and adolescents in Altai Krai suffering from diabetes mellitus were processed. Information is stored in the records in both structured and unstructured forms. Most of the valuable data, such as the dynamics of the disease course, patient complaints, patient’s life history, etc. are kept in natural language. The difficulty of processing text medical records is associated with a great number of abbreviations, synonyms and misprints, which makes it impossible to use a unified template. Therefore, the study is aimed at minimizing information losses while extracting knowledge by means of applying various text data mining methods. The practical outcome of this study is a database containing a large amount of valuable information on diabetes mellitus, various types of its clinical course and complications. The obtained data will be further used to build mining models for diagnosing and predicting the disease and its complications. To reach the goal of the research, we used the PostgreSQL DBMS and modern linguistically oriented software created within the framework of the Python programming language and its libraries: python-docx, natasha, Natural Language Toolkit (NLTK).
Improved Computation-Communication Trade-Off for Coded Distributed Computing using Linear Dependence of Intermediate Values

In large scale distributed computing systems, communication overhead is one of the major bottlenecks. In the map-shuffle-reduce framework, which is one of the major distributed computing frameworks, the communication load among servers can be reduced by increasing the computation load of each server, that is, there is a trade-off between computation load and communication load. Recently, it has been shown that coded distributed computing (CDC) improves this trade-off relationship by letting servers encode their intermediate computation results. The original CDC scheme does not assume any special structures on the functions that servers compute. However, in actual problems, these functions often have some structures, and the trade-off relation may be further improved by using that structures. In this paper, we propose a new scheme that further improves the trade-off relationship by utilizing the linear dependency structure of the intermediate computation results. The intermediate values computed in the map phase can be considered as vectors on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . In some applications, these intermediate values have a linear dependency and in such cases, it is sufficient for each server to send a basis of the linear subspace and linear combination coefficients. As a result, the proposed approach improves over the best-known computation-communication overhead trade-off in some applications.
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data

We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities. Traditional spectral clustering techniques discover clusters by processing a similarity matrix that reflects the proximity of objects. For multi-scale data, distance-based similarity is not effective because objects of a sparse cluster could be far apart while those of a dense cluster have to be sufficiently close. Following [16], we solve the problem of spectral clustering on multi-scale data by integrating the concept of objects' "reachability similarity" with a given distance-based similarity to derive an objects' coefficient matrix. We propose the algorithm CAST that applies trace Lasso to regularize the coefficient matrix. We prove that the resulting coefficient matrix has the "grouping effect" and that it exhibits "sparsity". We show that these two characteristics imply very effective spectral clustering. We evaluate CAST and 10 other clustering methods on a wide range of datasets w.r.t. various measures. Experimental results show that CAST provides excellent performance and is highly robust across test cases of multi-scale data.
Binary Separable Convolutional: An Efficient Fast Image Classification Method

The training and running of neural network require large computational space and memory space, which makes it difficult to deploy on a resource-constrained embedded sys-tems. To address this limitation, we introduce a two-stage pipeline: Depth separable, local binary. Our method is divided into two steps. Firstly, a deep separable convolution is applied to each input channel. Then, point-by-point convolution is applied to the feature map obtained by the filter. In the second step, we use local binarization method to initialize the filter corresponding to the input channel into sparse binary. In network training, the sparse binary filter remains fixed and only needs to train convolution of 1×1 size. Our experimental results show that, on the basis of approximate accuracy with the original network, we have reduced the number of convolution parameters by 9x to 10x, and reduced the training time and testing time to by 2x. Our compression method helps to deploy complex neural networks on resource-constrained embedded platform.
Learning Based Distributed Tracking

Inspired by the great success of machine learning in the past decade, people have been thinking about the possibility of improving the theoretical results by exploring data distribution. In this paper, we revisit a fundamental problem called Distributed Tracking (DT) under an assumption that the data follows a certain (known or unknown) distribution, and propose a number Data-dependent algorithms with improved theoretical bounds. Informally, in the DT problem, there is a coordinator and k players, where the coordinator holds a threshold N and each player has a counter. At each time stamp, at most one counter can be increased by one. The job of the coordinator is to capture the exact moment when the sum of all these k counters reaches N. The goal is to minimise the communication cost. While our first type of algorithms assume the concrete data distribution is known in advance, our second type of algorithms can learn the distribution on the fly. Both of the algorithms achieve a communication cost bounded by O(k log log N) with high probability, improving the state-of-the-art data-independent bound O(k log N/k). We further propose a number of implementation optimisation heuristics to improve both efficiency and robustness of the algorithms. Finally, we conduct extensive experiments on three real datasets and four synthetic datasets. The experimental results show that the communication cost of our algorithms is as least as $20%$ of that of the state-of-the-art algorithms.
Chinese Open Relation Extraction with Pointer-Generator Networks

Most of the traditional Chinese open relation extraction (Open RE) system exploit the syntactic, lexical and other language structure information obtained by natural language processing (NLP) tools from sentences to build hand-crafted patterns for extraction, which is easy to cause error propagation and affect the accuracy of extraction. In this paper, we propose an end-to-end abstract Chinese Open RE model based on the Pointer-Generator network, PGCORE. We employ the results extracted by the state-of-the-art pattern-based Chinese Open RE system as the training set of the model. Experimental results show that our method is outperforms the pattern-based several baselines system, which proves the feasibility and effectiveness of using deep learning models for Chinese Open RE.
Web Personalization Using Web Session for Web Usage Mining

In this internet era, sharing information, carrying out business and discharging service is an easy task. These are accomplished by social networking and web applications due to the existence of Web. The users on the web have high aspirations on reliability and many other aspects in nature. With this trend and explosion of information technology, web services and web based applications the WWW has become a golden mount with world's largest knowledge repository. As a result, WWW has profoundly influenced many aspects of user lives and brought changes in the fields of communication, business, sharing knowledge, shopping, and entertainment and so on. However, the abundant information on the web, which is structurally complex, non scalable and exponentially growing in nature, poses great challenges in the era of web mining research. In this paper using web session, personalized web pages are identified.
Adaptive Spatial Clustering for Multi-Dimensional Data and Its Cloud Model Representation

In view of the problem that the number of clusters need to be set manually, it is difficult to process the multi-dimensional data effectively, and the clustering results are not described effectively when the multi-dimensional data need to be clustered. This paper proposes a method of adaptive spatial clustering and its cloud model representation for the multi-dimensional data. This method can be used to cluster multi-dimensional spatial data, form qualitative description of clustering results, and realize the reconstruction and verification of qualitative description features. Through simulation experiments, this method can cluster data adaptively without the need to set the number of clusters. At the same time, it has a good ability to abstract and reconstruct digital features.
What is yopará? (Observations on paraguayan Spanish)

The article purports to summarize and rethink a considerable amount of facts collected by various researchers who studied the linguistic situation and the interplay of two languages, Spanish and Guarani, in modern Paraguay. Having revised a number facts pertaining to different levels of the language system, the author arrives at the conclusion that the linguistic phenomenon called yopará is emerging as a new language in the full sense of this word, its mixed origin notwithstanding, a real future national language now taking shape.