Reviewerly/exHarmony huggingface dataset:
Classification of Microsoft Office Vulnerabilities: A Step Ahead for Secure Software Development

Classification of software vulnerability no doubt facilitates the understanding of security-related information and accelerates vulnerability analysis. In the absence of proper classification, it hinders its understanding and also renders the strategy of developing mitigation mechanism for clustered vulnerabilities. Now, software developers and researchers have agreed on the fact that incorporating security in software engineering phases including requirement and design may yield maximum benefits. In this paper, we have attempted to classify software vulnerabilities of Microsoft Office, so that this can help in building secure software. Vulnerabilities are firstly classified on well-established security properties like authentication and authorization. Vulnerability data is collected from various authentic sources, including Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE). From these databases, only those vulnerabilities are included whose mitigation is possible at the design phase. Then, this vulnerability data is preprocessed to handle missing values and noise removal; further, the data is classified using various supervised machine learning techniques. Classification models are compared for three security metrics: integrity, confidentiality and availability. All the classifiers achieved highest accuracy for integrity.
F-SQL: Fuse Table Schema and Table Content for Single-Table Text2SQL Generation

Automatically parsing SQL queries from natural languages can help non-professionals access databases and improve the efficiency of information utilization. It is a long-term research issue and has recently received attention from the relevant communities. Although previous researches have provided some workable solutions, most of them only consider table schemas and natural language questions when parsing SQL queries, and do not use table contents. We observe that table contents can provide more helpful information for some user questions. In this paper, we propose a novel neural network approach, F-SQL, to focus on solving the problem of table content utilization. In particular, we employ the gate mechanism to fuse table schemas and table contents and get the more different representation about table schemas. We test this idea on the WikiSQL and TableQA datasets. Experimental results show that F-SQL achieves new state-of-the-art results on WikiSQL and TableQA.
Off-The-Shelf Based System for Urban Environment Video Analytics

This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.), The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
Face Recognition and Machine Learning at the Edge

Abstract The number of IoT is expected to reach 20 billion by year 2020. This is due to data that log in the sensors or cameras are all send to the cloud for further processing. Cloud computing is not able to support big data analytic anymore due to network bandwidth. Face recognition is chosen as a case study to demonstrate the challenges to shift the application to the edge. The objective of this project is to develop a face recognition system that is suitable to be used at the edge using a deep neural network. Secondly, investigate the performance in terms of model size, speed and inference time after different bit-width fixed point quantization on the weights of the network. Lastly, deploy the model to Raspberry Pi 3 and test the performance. The chosen data set is AT&amp;T. MATLAB is used to train the network in laptop with i5-7300 CPU while OpenCV-python is used to load and test the network in Raspberry Pi3 and laptop. The proposed system is designed by doing transfer learning on SqueezeNet to classify face. Fixed-point quantization is being applied to the weights of the layers to reduce the size of the model. From the experiment result, it is recommended to use 8-bit fixed-point quantization to the weights in all the layers in the model to compress the size of the network up to 2.5 times while maintaining the original accuracy 90%. That is only 1.1× speed up of the model on Raspberry Pi 3 after different bit-width weight quantization.
Adaptive Hierarchical Attention-Enhanced Gated Network Integrating Reviews for Item Recommendation

Many studies focusing on integrating reviews with ratings to improve recommendation performance have been quite successful. However, these works still face several shortcomings: (1) The importance of dynamically integrating review and interaction data features is typically ignored, yet treating these fusion features equally may lead to an incomplete understanding of user preferences. (2) Some forms of soft attention methods are adopted to model the local semantic information of words. As features thus captured may contain irrelevant information, the generated attention map is neither discriminatory nor detailed. In this paper, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">daptive</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ierarchical</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ttention-enhanced</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ated network integrating reviews for item recommendation</i> , named AHAG. AHAG is a unified framework to capture the hidden intentions of users by adaptively incorporating reviews. Specifically, we design a gated network to dynamically fuse the extracted features and select the features that are most relevant to user preferences. To capture distinguishing fine-grained features, we introduce a hierarchical attention mechanism to learn important semantic information features and the dynamic interaction of these features. Besides, the high-order non-linear interaction of neural factorization machines is utilized to derive the rating prediction. Experiments on seven real-world datasets show that the proposed AHAG significantly outperforms state-of-the-art methods. Furthermore, the attention mechanism can highlight the relevant information in reviews to increase the interpretability of the recommendation task. Source codes are available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/luojia527/AHAG</uri> .
DETECTION METHOD OF THE PROBABLE INTEGRITY VIOLATION AREAS IN FPGA-BASED SAFETY-CRITICAL SYSTEMS

The features of integrity monitoring of FPGA-based safety-critical systems are considered. Hardware Trojans are distinguished as one of the most dangerous types of malicious integrity violation of FPGA-based systems. The study has proved that Hardware Trojans can be implanted into the system (or system project) during its planned modification. In particular, it happens when the integrity monitoring, based on the hash sum usage, does not operate. Before running the integrity monitoring, one should ensure that Hardware Trojans were not implanted. Authors proposed the method for detecting the hardware Trojans location in the space of FPGA-based components of safety-critical systems. The method is based on the analysis of addressing to the values of calculated LUT units for these components in the normal and emergency modes of system operation. The hardware module for addressing the registration in accordance with the proposed method is implemented.
Model of process and model of natural language processing system

Abstract Natural language processing is the set of methods for making human language accessibility from computers. An approach that facilitates search in database even for casual non-technical literate users is to use a natural language interface (IEE) for databases. It is important for these IEEs to be intelligent and reliable enough to interpret the most complex sentences most intelligently and effectively enough to deliver results in the shortest possible time. The advantage of natural language interfaces is that they are accessible to a wide range of end users in order to “speak” to the database. Introduction. The solution to the problem of natural-language understanding can be accomplished in two steps: linguistic and database processing. In the first stage of language processing, the natural language query is linked and translated to the corresponding SQL query by appropriate production rules, methods and functions. In the second processing stage, the database is accessed and manipulated and the SQL query is executed by the system. The study presents a research in the field of computational linguistics on a theoretical process model and a natural language processing system model. The process model itself represents knowledge and reflects the conceptual organization of human memory, as well as its flexibility and clarity.
BIG-SWSDM: BIpartite Graph Based Social Web Service Discovery Model

With the increasing number of similar web services nowadays, the need to satisfy the complex user requirements and locate relevant services remain necessary. As a complex and challenging task, many approaches have been proposed. Nevertheless, they totally neglect the contribution of the social dimension. The mix of two domains social computing with service oriented computing opens the door to new discovery schemes. It gives birth to a new notion Social Web Services. In fact, integrating the social aspect into web services can benefit them to become active entities that can collaborate, compete or substitute each other. In this paper, we present the second step of our social web service discovery model that operates on a bipartite graph with user-user, user-service and service-service relationships and employs new metrics to evaluate the ability of a user to help the service requester to satisfy his needs.
Methods of Classifying Foreign Language Communicative Competence Using the Example of Intending Primary School Teachers

This article deals with the methods of classifying communicative competence. Finding effective ways to evaluate the level of intending primary school teachers' foreign language communicative competence is becoming increasingly relevant today, as objectifying the measurement process through feedback provides an opportunity to coordinate the developing that competence components (motivational, cognitive, communicatively active, reflexive) and further improving the students' levels of foreign language communicative competence. In order to effectively evaluate developing the foreign language communicative competence, it is necessary to focus not only on explicitly expressed, but also on implicitly expressed traits and qualities of an intending professional. It means that the assessment, in addition to traditional parameters, should determine those personal qualities that are hidden for direct observation and which are only indirectly manifested in the professionally oriented communicative activity of the individual. All above mentioned information necessitate the development of a special assessment scale, the creation of which is objectively difficult due to the complexity of identifying and describing implicit parameters of professionally relevant components of communicative competence. During this process it is important to use the more sophisticated mathematical methods and information technologies. The minimax classifications, method of k-means, Bayesian classification are proposed with the aim to assess the levels of students' (intending primary school teachers') foreign language communicative competence. Students' ratings were determined by the hierarchy analysis method. Decision-making procedures are used to select one of two or four possible options.
Milestone-Driven Agile Execution

This chapter introduces a hybrid software development framework, called Milestone-Driven Agile Execution, in which the empirical process control and the just-in-time planning of tasks of agile development are retained but the prioritization of the backlog is done according to a macro or strategic plan that drives the execution of the project. Selecting work items from the product backlog according to a plan instead of following the immediate concerns of a product owner adds visibility, predictability, and structure to the work of the team while preserving the adaptive advantages of agile development.
Learning distributed communication and computation in the IoT

In distributed, cooperative Internet of Things (IoT) settings, sensing devices must communicate in a resource-aware fashion to achieve a diverse set of tasks, (i.e., event detection, image classification). In such settings, we continue to see a shift from reliance on cloud-centric to edge-centric architectures for data processing, inference and actuation. Distributed edge inference techniques address real-time, connectivity, network bandwidth and latency challenges in spatially distributed IoT applications. Achieving efficient, resource-aware communication in such systems is a longstanding challenge. Many current approaches require complex, hand-engineered communication protocols. In this paper, we present a novel scalable, data-driven and communication-efficient Convolutional Recurrent Neural Network (C-RNN) framework for distributed tasks. We provide empirical and systematic analyses of model convergence, node scalability, computation-cost and communication-cost based on dynamic network graphs. Further to this, we show that our framework is able to solve distributed image classification tasks via automatically learned communication.
Integrated optimization model and algorithm for pattern generation and selection in logical analysis of data

In this paper, we present a new integrated optimization model and a greedy algorithm for generating patterns, directly derived from original data instead of binarized data, in logical analysis of data (LAD). Pattern generation, following data discretization (binarization) and support set selection to handle non-binary data, is a building block that largely influences LAD classification. These stand-alone steps are generally considered optimization problems, which are difficult to solve and make the LAD procedure very tedious. To this end, we propose a new mixed-integer linear program, in which data discretization and support set selection are integrated into a single pattern generation optimization model, aiming to generate multiple logical patterns to cover observations maximally in the original data space. Furthermore, we develop a greedy search algorithm, in which the optimization model is reduced and solved iteratively to efficiently generate patterns. We then examine the effectiveness of the generated patterns in both one-class and large-margin LAD classifiers. The computational results for simulated and real datasets demonstrate the competitive performance in terms of classification accuracy in a relatively short runtime compared with previously developed pattern generation methods and other state-of-the-art machine learning algorithms.
On comparing locations of two-parameter exponential distributions using sequential sampling with applications in cancer research

In this paper, we develop testing procedures for hypotheses regarding the difference of location parameters from two independent two-parameter exponential populations with unknown but proportional scale parameters. We design appropriate two-stage and purely sequential procedures to determine the appropriate sample sizes while controlling both type-I and type-II error probabilities at or below the preassigned levels α, β respectively. We prove that both two-stage and purely sequential testing strategies enjoy a number of practically appealing properties. Extensive sets of computer simulations validate our theoretical findings. We illustrate the testing methodologies along with sampling procedures on a cancer studies data.
Searching the Web for Cross-lingual Parallel Data

While the World Wide Web provides a large amount of text in many languages, cross-lingual parallel data is more difficult to obtain. Despite its scarcity, this parallel cross-lingual data plays a crucial role in a variety of tasks in natural language processing with applications in machine translation, cross-lingual information retrieval, and document classification, as well as learning cross-lingual representations. Here, we describe the end-to-end process of searching the web for parallel cross-lingual texts. We motivate obtaining parallel text as a retrieval problem whereby the goal is to retrieve cross-lingual parallel text from a large, multilingual web-crawled corpus. We introduce techniques for searching for cross-lingual parallel data based on language, content, and other metadata. We motivate and introduce multilingual sentence embeddings as a core tool and demonstrate techniques and models that leverage them for identifying parallel documents and sentences as well as techniques for retrieving and filtering this data. We describe several large-scale datasets curated using these techniques and show how training on sentences extracted from parallel or comparable documents mined from the Web can improve machine translation models and facilitate cross-lingual NLP.
Improving Accuracy of Automatic Derivation of State Variables and Transitions from a Japanese Requirements Specification

A requirement specification for software is usually described in a natural language and thus may include sentences containing ambiguity and contradiction. Design errors due to ambiguous expressions and contradictions are often found later in the development process. These ambiguous sentences would force the developer to go back to the design process again. In order to prevent this kind of rework, a method of automatically converting a requirements specification written in Japanese to a state transition model is desired to help detect ambiguity and contradiction points of the specification. Our previous research proposed a method to extract the sentence elements necessary for creating a state transition diagram by utilizing morphological analysis techniques and sentence parsing. The extracted elements, however, contained many redundant or unnecessary elements. This paper proposes an improved method to remove such redundant or unnecessary elements by mainly improving parsing rules. This paper also reports the effectiveness of the proposed method by showing the extracted elements necessary for creating a state transition diagram.
Novel user authentication method based on body composition analysis

Abstract Authentication is the process of confirming one’s identity. There is a steadily growing need to protect confidential, especially financial, data, as banks provide their services online through their ubiquitous systems. This paper presents a novel authentication method based on the analysis of body composition. A trusted system that relies on the biometric authentication has been designed, implemented, and evaluated, showing a false accept rate (FAR) of 0%, while its false reject rate (FRR) is 2.65%. As the proposed solution requires virtually no special action from the user during the authentication process, it can be seen as suitable for incorporation into existing multifactor authentication solutions.
A semantic relatedness preserved subset extraction method for language corpora based on pseudo-Boolean optimization

As language corpora have been playing an increasingly important role in the field of Artificial Intelligence (AI) research, lots of extremely large corpora are created. However, a larger corpora size not only increases power and accuracy but also brings redundancy. Therefore, researchers began to emphasize the study of appropriate subset extraction methods. Due to the trade-off between data sufficiency and redundancy, a group of interesting and challenging problems are emerged that are studied in this paper: (1) How to make the resulting subset include as much data as possible under some necessary constraints? (2) How to preserve the potential useful semantic relatedness included in the original corpora while reducing the size of the corpora? For these two problems, existing work mainly focuses on the methods to construct particular subsets for special usage. These methods are limited in their focus. In this paper, we try to address the problems listed above. First, considering the cubic and binary semantic relatedness among tokens, we construct a general system model and formulate the mix problem as a cubic pseudo-Boolean optimization problem. Then, by analyzing the characteristics of the objective function, we transfer the problem into the maximum flow problem of a corresponding graph. Third, we propose a new algorithm by introducing discrete Lagrangian iteration method. We prove that the objective function is supermodular, which allows us to use fast minimum cut algorithms in each iteration step to propose another fast algorithm. Finally, we experimentally validate our new algorithms on several randomly created corpora.
Effects of Graph Pooling Layers on Classification with Graph Neural Networks

With the rise of graph neural networks, sometimes also referred to as geometric deep learning, a range of new types of network layers have been introduced. Since this is a very recent development, the design of new architectures relies a lot on intuition and trial-and-error. In this paper, we evaluate the effect of adding graph pooling layers to a network, which down-sample graphs, and evaluate the performance on three different datasets. We find that especially for smaller graphs, adding pooling layers should be done with caution, as they can have a negative effect on the overall performance.
SciTokens SSH: Token-based Authentication for Remote Login to Scientific Computing Environments

SciTokens SSH is a pluggable authentication module (PAM) that uses JSON Web Tokens (JWTs) for authentication to the Secure Shell (SSH) remote login service. SciTokens SSH supports multiple token issuers with local token verification, so scientific computing providers are not forced to rely on a single OAuth server for token issuance and verification. The decentralized design for SciTokens SSH was motivated by the distributed nature of scientific computing environments, where scientists use computational resources from multiple providers, with a variety of security policies, distributed across the globe.