Reviewerly/exHarmony huggingface dataset:
Dynamically Weighted Multi-View Semi-Supervised Learning for CAPTCHA

With the development of Optical Character Recognition and artificial intelligence technologies, the security of Behavioral Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) has become an increasingly difficult task. In order to prevent malicious attacks and maintain network security, most existing works on CAPTCHA are to construct a fine binary classifier model but are not yet capable of detecting new attack means during confrontation. This motivates us to propose a Dynamically Weighted Multi-View Semi-Supervised Learning, dubbed as DWMVSSL method, to relieve this problem. More specifically, our proposed method extracts hidden patterns from multiple perspectives and updates the view weighting dynamically which can constantly detect new attack means. In addition, due to existing some redundant feature in views, we design a Filter Artificial Bee Colony method, named as FABC for feature selection which can efficiently reduce the impact of high dimensional features. The experimental results show that, compared the existing representative baseline methods, our DWMVSSL method can effectively detecting new attacks on confrontation.
A Discussion of the Impact of Judgmental Heuristics on Elicitation during Requirements Engineering

A stakeholder decides which information to share during requirements elicitation. These decisions are made under uncertainty: stakeholders may not have precise, concrete, and complete information about their own expectations from, or about the environment of the system-to-be, they may misunderstand information which a requirements engineer gives them, and they may not have perfect knowledge about the various opportunities that the current technology may offer. Stakeholders' decision-making is therefore likely to involve the use of heuristics. Understanding if stakeholders do indeed use heuristics during requirements elicitation, and if so, then which ones, should help understand requirements elicitation better, and design methods which can stimulate positive heuristics (those which help elicit relevant requirements) and mitigate negative ones. This paper defines what an elicitation heuristic may be, and proposes a set of research questions under the form of a matrix for the theoretical and empirical study of elicitation heuristics.
Core Clustering as a Tool for Tackling Noise in Cluster Labels

Real-world data sets often contain mislabelled entities. This can be particularly problematic if the data set is being used by a supervised classification algorithm at its learning phase. In this case, the accuracy of this classification algorithm, when applied to unlabelled data, is likely to suffer considerably. In this paper, we introduce a clustering-based method capable of reducing the number of mislabelled entities in data sets. Our method can be summarised as follows: (i) cluster the data set; (ii) select the entities that have the most potential to be assigned to correct clusters; (iii) use the entities of the previous step to define the core clusters and map them to the labels using a confusion matrix; (iv) use the core clusters and our cluster membership criterion to correct the labels of the remaining entities. We perform numerous experiments to validate our method empirically using k-nearest neighbour classifiers as a benchmark. We experiment with both synthetic and real-world data sets with different proportions of mislabelled entities. Our experiments demonstrate that the proposed method produces promising results. Thus, it could be used as a preprocessing data correction step of a supervised machine learning algorithm.
NeoLOD: A Novel Generalized Coupled Local Outlier Detection Model Embedded Non-IID Similarity Metric

Traditional generalized local outlier detection model (TraLOD) unifies the abstract methods and steps for classic local outlier detection approaches that are able to capture local behavior to improve detection performance compared to global outlier detection techniques. However, TraLOD still suffers from an inherent limitation for rational data: it uses traditional (Euclidean) similarity metric to pick out the context/reference set ignoring the effect of attribute structure. i.e., it is with the fundamental assumption that attributes and attribute values are independent and identically distributed (IID). To address the issue above, this paper introduces a novel Non-IID generalized coupled local outlier detection model (NeoLOD) and its instance (NeoLOF) for identifying local outliers with strong couplings. Concretely, this paper mainly includes three aspects: (i) captures the underlying attribute relations automatically by using the Bayesian network. (ii) proposes a novel Non-IID similarity metric to capture the intra-coupling and inter-coupling between attributes and attribute values. (iii) unifies the generalized local outlier detection model by incorporating the Non-IID similarity metric and instantiates a novel NeoLOF algorithm. Results obtained from 13 data sets show the proposed similarity metric can utilize the attribute structure effectively and NeoLOF can improve the performance in local outlier detection tasks.
Fake or Fact? Theoretical and Practical Aspects of Fake News

The phenomenon of fake news is nothing new. It has been around as long as people have had a vested interest in manipulating opinions and images, dating back to historical times, for which written accounts exist and probably much beyond. Referring to it as post-truth seems futile, as there’s probably never been an era of truth when it comes to news. More recently, however, the technical means and the widespread use of social media have propelled the phenomenon onto a new level altogether. Individuals, organizations, and state-actors actively engage in propaganda and the use of fake news to create insecurity, confusion, and doubt and promote their own agenda – frequently of a financial or political nature. We discuss the history of fake news and some reasons as to why people are bound to fall for it. We address signs of fake news and ways to detect it or, to at least become more aware of it and discuss the subject of truthfulness of messages and the perceived information quality of platforms. Some examples from the recent past demonstrate how fake news has played a role in a variety of scenarios. We conclude with remarks on how to tackle the phenomenon – to eradicate it will not be possible in the near term. But employing a few sound strategies might mitigate some of the harmful effects.
Deniable Secret Handshake Protocol - Revisited

The notion of deniability ensures that the transcript generated in an interactive protocol does not yield any evidence of the interaction. In the context of key-exchange protocols for secure message transmission, the notion of deniability is well-explored. On the other hand, a secret handshake protocol enables a group of authorized users to establish a shared secret key and authenticate each other. Recently, a framework for deniable secret handshake is proposed by Tian et al. in ISPEC 2018. We analyze the protocol, show three flaws and give solutions to prevent them.
Privacy Preserving kNN Spatial Query with Voronoi Neighbors

With the increased demand for outsourcing databases, there is a demand to enable secure and efficient communications. The concern regarding outsourcing data is mainly providing confidentiality and integrity to the data. This paper proposes a novel solution to answering kNN queries at the cloud server over encrypted data. Data owners transform their data from a native domain to a new domain to assist in nearest neighbors’ classification. The transformation is achieved by Voronoi diagram, which transforms the data space into numerous small regions, simplifying the nearest neighbor search. However, because the regions that make up a Voronoi diagram are irregularly shaped, the search through the network becomes hard to accomplish. Thus, the solution includes a Grid-based indexing approach for the Voronoi diagram to expedite the kNN search. Additionally, a strong encryption algorithm, like AES, is used to encrypt the data objects being sent from the data owner to the cloud. An authorized user sends encrypted kNN queries to the cloud where the query is processed over encrypted data. The cloud service provider utilizes the proposed indexing scheme to identify a superset of the nearest neighboring objects to be sent back to the user. The user possessing a copy of the encryption key decrypts the superset of k nearest neighbors and filters the exact k objects.
Unification of Gravity and Electromagnetism

Gravity and electromagnetism are two sides of the same coin, which is the clue of this unification. Gravity and electromagnetism are representing by two mathematical structures, symmetric and antisymmetric respectively. Einstein gravitational field equation is the symmetric mathematical structure. Electrodynamics Lagrangian is three parts, for electromagnetic field, Dirac field and interaction term. The definition of canonical energy momentum tensor was used for each term in Electrodynamics Lagrangian to construct the antisymmetric mathematical structure. Symmetric and antisymmetric gravitational field equations are two sides of the same Lagrangian
Social Cyber Forensics (SCF): Uncovering Hidden Relationships

In this chapter, we will introduce the concept of social cyber forensics Social cyber forensics (SCF) (SCF) and its usability. Then, we will introduce a tool, i.e., Maltego Maltego that can be used to study the cross-media affiliation Cross-media affiliation and uncover hidden relations among different ODGs Online deviant groups (ODGs) . In Sect. 4.2 we will provide three stepwise methodologies that can be followed to reach the desired outcome of analysis (e.g., uncovering the hidden relationship between Twitter accounts and a set of websites or blog sites, websites or blog sites and other websites or blog sites, and infer the ownership of a set of websites or blog sites). These methodologies have been tested during many cyber propaganda Propaganda campaigns to associate and infer the relationships between different online groups. We also provide hands-on exercises to practice each of the methodologies introduced.
IDEL: In-Database Neural Entity Linking

We present a novel architecture In-Database Entity Linking (IDEL), in which we integrate the analytical RDBMS MonetDB with neural text mining abilities. To the best of our knowledge, this is the first defacto implemented system integrating entity-linking in a database. IDEL represents text and relational data in a joint vector space with neural embeddings and can compensate errors with ambiguous entity representations. To detect matching entities, we propose a novel similarity function based on joint neural embeddings which are learned via minimizing pairwise contrastive ranking loss. This function utilizes high dimensional index structures for fast retrieval of matching entities. The system achieves zero cost for data shipping and transformation by utilizing MonetDB's ability to embed Python processes in its kernel and exchange data in NumPy arrays. We report from experiments on the WebNLG corpus on ten entity types high F-measures and low execution times.
A RETROSPECTIVE ANALYSIS OF THE TERM AND PHENOMENON OF "VIRTUAL EDUCATIONAL SPACE" IN THE CONTEXT OF IMPLEMENTING OF INTERNATIONAL EDUCATIONAL ACTIVITIES

The retrospective analysis of the term and phenomenon of "virtual educational space" revealed the need to develop a theoretical model of using the virtual educational environment, identifying ways of its effective application, where the implementation of international educational activities is not an exception. In addition, the virtualization of the educational environment also causes a certain number of threats regarding the risks of functioning in the conditions of real society and the self-identity of the individual due to, so to speak, virtual transitions from one virtual reality to another and existence in reality.
Predicting and Preventing Dangerous Events via Video Surveillance Using a Robotic Platform

Our goal is to improve and extend our earlier, prototypical work on event-detection platforms, which was able to take immediate action by means of a robotic platform, when a potentially dangerous event was detected. Here we go further by enhancing and extending the system in order to not only detect instances of potentially dangerous events, but to try to predict them before they actually occur. This allows the robot to carry out specific preventive actions to stop them happening in the first place.
Differential Evolution in Agent-Based Computing

Evolutionary multi-agent systems (EMAS) turned out to be quite efficient technique for solving complex problems, both benchmark ones (as well-known multi-dimensional functions, e.g. Rastrigin, Schwefel etc) and more practical ones (like Optimal Golomb Ruler or Low Autocorrelation Binary Sequence). However the already classic design of the EMAS (these metaheuristics have been developed for over 15 years) has still many places for improvement. Hybridization is one of such means, and it turns out that incorporating Differential Evolution mechanisms into EMAS (altering the reproduction strategy by making it more social-aware) improves the accuracy of the search. This paper deals with discussion of selected means for hybridization of EMAS with DE, and provides an insight into the efficacy of the novel algorithm compared with classic techniques based on multidimensional benchmark problems.
High Level Synthesis for Symmetric Key Cryptography

Classification of the major cryptographic functions on the basis of their under-lying basic computational elements yields simplicity and scalability in the design of cryptographic embedded systems. Especially, it benefits their high level synthesis, with performance as good as the hand crafted designs. This chapter discusses the various steps in the HLS tool for block ciphers (called RunFein) and stream ciphers (called RunStream). Several design points in the scope of architectural customizations available to the HLS tool are elaborated. A thorough benchmarking to compare against the hand crafted solutions reveals their performance to be at-par with hand-optimized HDL implementations. The results can be accessed as [1–3].
Control and Physical Intelligence

Control and physical intelligence are reviewed in the context of engineered and biological systems. First, the general control problem is revised. Then, the popular topics of engineered systems are addressed including proportional-integral-derivative control approach, time delays, various sources of errors and uncertainties, stability concept, feedback linearization, sliding control, adaptive control, Bellman's curse of dimensionality and various attempts to resolve it, artificial intelligence, machine learning, data mining, etc. The rest of chapter is focused on control aspects of biological system, that is, central and peripheral nervous system and its constituents.
Dataset of solar energy potential assessment for Adama city (Ethiopia)

This paper focuses on estimation of available solar radiation from sunshine hour duration. Sunshine hour duration data from 2013 to 2017 G.C recording from Adama metrology agency, average daily global radiation in horizontal and tilt surface for global, diffuse and beam radiation are calculated also hourly global radiation and diffuse radiation data are calculated in tilt surface reach. Finally global and diffuse radiation data for January 5, 2017 and July 5, 2017 are calculated.
Impact Vertices-Aware Diffusion Walk Algorithm for Efficient Subgraph Pattern Matching in Massive Graphs

Subgraph pattern matching is a basic building block for many applications.Where to commence the pattern matching task and how to proceed are fundamental issues in massive graphs.In this paper, we propose the most impact vertices in view of a query graph and diffusion walk on data graph.We present a novel impact vertices-aware diffusion walk algorithm, a distributed algorithm named DiffWalk, for subgraph pattern matching.Our algorithm employs the most impact vertices from a query graph to locate the initial search position and then proceeds to traverse a large-scale data graph by diffusion walk.We give theoretical analyses based on probability inference and spectral graph, which prove that graph pattern matching beginning at the most impact vertices could prevent comparison overhead by low-probability events first, also prove that diffusion walk could traverse graph efficiently.We have performed a range of experiments that demonstrate our algorithm efficiency both in running time and communication size.
High-Dimensional Quantum Cryptography using Twisted Photons; from the Laboratory to realistic conditions

We investigate various high-dimensional quantum cryptographic protocols using twisted photons and their feasibility in realistic conditions. In particular, we perform quantum key distribution in an intra-city freespace link and an underwater channel.
PCA Kernel Based Extreme Learning Machine Model for Detection of NS1 from Salivary SERS Spectra

Use of NS1 as a biomarker in saliva has led to the non-invasive and early detection of Flaviviridae related diseases. Saliva is preferred as medium of detection because of its advantages such as non-invasive, painless and easy to collect. Work here intends to compare the performance of KELM classifier with linear and RBF kernels for classification of NS1 from salivary SERS spectra. Prior to KELM, PCA with different termination criteria (Cattle Scree test, CPV and EOC) are used to extract important features and reduce the dimension of SERS spectra dataset. Regularization coefficient (C-value) for linear kernel and Regularization coefficient (C-value) and $$ \gamma $$ -value for RBF kernel are varied to find the optimum KELM classifier model. For linear kernel, 100% accuracy, precision, sensitivity, specificity is achieved for Linear model with EOC criterion and C-value set to 0.1, 0.2, 0.5, 1 and 2. For RBF kernel, 100% performance of accuracy, precision, sensitivity and specificity is achieved with RBF model with EOC criterion and values of 0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8 and 1. The C-value is fixed to 1. The best Kappa value of 1 is obtained when all performance indicators scored 100%. For both Linear-KELM and RBF-KELM, EOC termination criterion gives the highest performance. It also observed that KELM classifier is data dependent.
Analysis of Block-Level Data Deduplication on Cloud Storage

Data storage in the cloud is of prime importance for organizations, individuals, business as the storage of data is increasing exponentially. In the modern era, multiple data blocks of files across the sectors of storage disk are duplicated. Deduplication is a common technique for reducing a large amount of duplicate data. Deduplication storage optimization technique is an efficient method of reducing the chunks with pointer address mapping single instance across the storage data disk. Network transfer protocol for deduplication is major impact factor for performance. Network bandwidth, disk storage optimizations are parameters for real time data transfer in the deduplication protocol. In this analysis, we are examining the secure deduplication on cloud storage data on fixed block level and file level deduplication with new Protocol for transferring the data to cloud storage. Client–Server data transfer model architecture for deduplication technique will be analyzed for the new protocol data transfer.
Automated Testing of Vehicle Instrument Cluster Based on Computer Vision

With advancement of technologies, instrument cluster is become more complex. Conventional manual testing and validation is difficult to cover all the test cases to provide flawless delivery of product within restricted development timescale. As for now, we are proposing to use computer vision system in automotive manufacturing for automated design validation testing process. The purpose of the inclusion of computer vision system is to replace the conventional design validation testing process which is time consuming and extremely labor intensive. The speedometer, tachometer, fuel gauge and temperature gauge are inspected by comparing the accuracy of the pointer detected using the developed algorithms with the pointer position displayed on the meter. Besides that, the signal indicators status can be inspected using pixel intensity test. Under the assumption of controlled light surrounding and fixed position of the camera, pixel intensity can produce accurate results. The deviation error of needle gauge test and signal indicator test are 2.3%, those error occur while there is some noises influence the threshold value. Besides that, processing time of computer vision is within 0.5 s which is quite efficiency in testing process. In conclusion, the machine vision system is able to help for spectating the automated instrument cluster testing process.
Human motion data editing based on a convolutional automatic encoder and manifold learning

Technology of human motion capture has been widely used in digital entertainment field. Editing the existing large amount of human motion capture data, correcting and eliminating motion distortion caused by noise and other defects have important value and significance for data reuse. In this paper, data processing is carried out based on convolutional automatic encoder and manifold learning. The popular structure of human motion data was learned by a one-dimensional time domain convolution automatic encoder, in which the hidden unit of the automatic encoder represents motion data. Three constraints were used to overcome the problem that the hidden unit has too much motion editing range. The data to be processed in this paper has no limit on the number of motions. The proposed method can process large data sets in parallel and automatically perform manifold learning without manual labelling and segmentation. In the final, comparative experiments based on a variety of damaged motion data have been carried out. The results showed that the proposed method can effectively reduce the error of the original motion data, and has achieved good results in both objective evaluation and subjective evaluation.
Detecting Basic Level Categories by Term Weighting and Feature Entropy

With the explosive growth of wide variety resources in the real world, data structure mining becomes a meaningful subject. In cognitive psychology, there is a family of categories called basic level categories. This method can reflect natural categories of corpus faithfully. These categories represent the most nature level; neither too general nor too specific. People frequently prefer to use basic level concepts in their daily life. Basic level concepts are the abstraction of basic level categories. According to the study of cognitive psychology, we find that basic level categories play an important role in structural hierarchy relationship for human to understand. Existing methods can find out basic level categories in corpus but cannot work in continuous datasets. This paper proposed a method which can improve the similarity representation of category utility and help finding basic level categories not only in text datasets but also in continuous datasets. Our experiments demonstrate that our method has good performance in both two kinds of datasets than mainstream model.