Reviewerly/exHarmony huggingface dataset:
Using convolutional neural network autoencoders to understand unlabeled data

Gaining insight from unlabeled data is a widespread, challenging problem with many immediate applications. Clustering, dimensionality reduction for visualization, and anomaly detection are unsupervised learning solutions to this problem. Typically, the efficacy of these methods relies on obtaining sufficient amounts of data, presenting many challenges to cases where only limited data exists. In this work, we demonstrate that deep convolutional autoencoders can comfortably perform these tasks either directly or through manipulations of the latent space in a limited data setting. Clustering on common benchmark datasets produces comparable results to the current state-of-the-art in unsupervised classification. Clustering the activation maps of the encoding layers results in a form of unsupervised image segmentation with limited two-dimensional data. Visualizing the activation maps through dimension reduction demonstrates the possibilities of anomaly detection and semi-supervised learning. We are currently utilizing our versatile autoencoder to explore the ambitious task of finding anomalous and/or inconspicuous objects from single images.
A Proposal of Two Histogram Circuits to Calculate Similarities between Video Frames Using FPGAs

This paper describes two hardware schemes for calculating luminosity histograms using FPGAs. The first circuit makes extensive use of the embedded RAM blocks present in many FPGA models. The second alternative is a parallel structure of accomulators that can be easyly adapted to any input bus width. During the histogram computation, each processed pixel increments the value of the register corresponding to its luminance level. Therefore, if several pixels are evaluated at the same time, writing conflicts can be generated when a specific luminosity register is updated by more than one pixel. In the two proposed architectures these collision problems are eliminated. The calculation is made directly from the DC coefficients of the compressed video. This fact minimizes data bandwidth per frame, allowing a fast determination of similarity. The presented histogram circuits are part of an FPGA-based custom processor to calculate the similarity between two video frames by cross-correlating their histograms.
Invited Paper: Ensuring Responsible Outcomes from Technology

We attempt to make two arguments in this essay. First, through a case study of a mobile phone based voice-media service we have been running in rural central India for more than six years, we describe several implementation complexities we had to navigate towards realizing our intended vision of bringing social development through technology. Most of these complexities arose in the interface of our technology with society, and we argue that even other technology providers can create similar processes to manage this socio-technological interface and ensure intended outcomes from their technology use. We then build our second argument about how to ensure that the organizations behind both market driven technologies and those technologies that are adopted by the state, pay due attention towards responsibly managing the socio-technological interface of their innovations. We advocate for the technology engineers and researchers who work within these organizations, to take up the responsibility and ensure that their labour leads to making the world a better place especially for the poor and marginalized. We outline possible governance structures that can give more voice to the technology developers to push their organizations towards ensuring that responsible outcomes emerge from their technology. We note that the examples we use to build our arguments are limited to contemporary information and communication technology (ICT) platforms used directly by end-users to share content with one another, and hence our argument may not generalize to other ICTs in a straightforward manner.
Towards extending the SWITCH platform for time-critical, cloud-based CUDA applications: Job scheduling parameters influencing performance

SWITCH (Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications) allows for the development and deployment of real-time applications in the cloud, but it does not yet support instances backed by Graphics Processing Units (GPUs). Wanting to explore how SWITCH might support CUDA (a GPU architecture) in the future, we have undertaken a review of time-critical CUDA applications, discovering that run-time requirements (which we call ‘wall time’) are in many cases regarded as the most important. We have performed experiments to investigate which parameters have the greatest impact on wall time when running multiple Amazon Web Services GPU-backed instances. Although a maximum of 8 single-GPU instances can be launched in a single Amazon Region, launching just 2 instances rather than 1 gives a 42% decrease in wall time. Also, instances are often wasted doing nothing, and there is a moderately-strong relationship between how problems are distributed across instances and wall time. These findings can be used to enhance the SWITCH provision for specifying Non-Functional Requirements (NFRs); in the future, GPU-backed instances could be supported. These findings can also be used more generally, to optimise the balance between the computational resources needed and the resulting wall time to obtain results.
The Power of Temporal Features for Classifying News Articles

Temporal information extracted from texts and normalized to some standard format has been exploited in a variety of tasks such as information retrieval and question answering. Classifying documents into categories using temporal features has not yet been tried. Such a method might be particularly valuable when classifying sensitive texts such as patient records, i.e., whenever the pure content of the documents should not be used for the classification. In this paper, we describe, as a proof-of-concept, our work on classifying news articles exploiting only features defined over extracted and normalized temporal expressions. Our evaluation of two classification models on large German and English news archives shows promising results and demonstrates the discriminative power of temporal features for topically classifying text documents.
Interactive Blackboard for Web-based Real-time Tutoring System

In this paper we provide the design and implementation of a web-based real-time interactive blackboard that can be used in a web-based tutoring system. The goal of the tool is to help moving the classroom experience to the web, where teachers and students can communicate with each other in real-time by voice and by writing and drawing on the same blackboard. The main advantage of this system is to reduce tutoring cost by saving the time required for students and teachers to meet. Further, it allows teachers to tutor students from different cities or even different countries. The system is designed to be a module in a tutoring system environment. HTML5 canvas and WebSockets is used in this implementation. The complete code is available on GitHub code hosting website.
The Controversy Surrounding the Application of Projective Geometry to Stereo Vision

Although the success of the projective geometry applications in stereo vision, a number of criticisms have been raised in the literature about its use. Most of them concern the performance of the eight-point algorithm that is used to find the fundamental matrix F. And few directly target the application of the projective geometry in computer vision.
Towards Providing Relevant Digital Signage Advertisement to a Group of Users Based on Users' Interests Investigation

Nowadays an advertisement becomes more and more personal. Usually, internet data mining algorithms collect information about user's activities, analyze it and offer the most relevant context advertisement. But it is much more complicated to find information to show to a group of viewers in public places. This happens due to privacy restrictions and lack of algorithms that would allow finding mutual interests for a group of users. In this article, an approach to solving these problems through analysis of multiple visual viewers' characteristics is proposed.
Blockchain 2.0

Blockchain is a shared and distributed ledger across an open or private processing system that expedites the process of recording transactions and data management in a business network. It empowers the design of decentralized transactions, smart contracts, and intelligent assets that can be managed over internet. It formulates the revolutionary decision-making governance systems with more egalitarian users, and autonomous organizations that can control over internet without any third-party involved. This disruptive technology has tremendous opportunities that open the doors to detract the power from centralized authorities in the sphere of communications, business, and even politics or law. This chapter outlines an introduction to the blockchain technologies and its decentralized architecture, especially from the perspective of challenges and limitations. The objective is to explore the current research topics, benefits, and drawbacks of blockchain. The study explores its potential applications for business and future directions that is all set to transfigure the digital world.
Weakly Supervised, Data-Driven Acquisition of Rules for Open Information Extraction

We propose a way to acquire rules for Open Information Extraction, based on lemma sequence patterns (including potential typographical symbols) linking two named entities in a sentence. Rule acquisition is data-driven and requires little supervision. Given an arbitrary relation, we identify, in a large corpus, pairs of entities that are linked by the relation and then gather, score and rank other phrases that link the same entity pairs. We experimented with 81 relations and acquired 20 extraction rules for each by mining ClueWeb12. We devised a semi-automatic evaluation protocol to measure recall and precision and found them to be at most 79.9% and 62.4% respectively. Verbal patterns are of better quality than non-verbal ones, although the latter achieve a maximum recall of 76.5%. The strategy proposed does not necessitate expensive resources or time-consuming handcrafted resources, but does require a large amount of text.
An Approach for Mapping the Aspect State Models to Aspect-Oriented Code

Model-driven code generation allows rapid generation of precise code thus reducing the development effort and the delivery time. Consequently, model-driven code generation has been a topic of interest in varying perspectives. While model-driven code generation has been explored well in many domains, its full potential has not been exploited in the context of aspect-oriented software development. The existing approaches have mainly focused on code generation from class diagrams only. Code generation from class diagrams is straightforward as majority of the constructs involved in these diagrams are directly mapped to those in programming languages. However, code generated using class diagrams is limited to skeletons of classes and methods only and does not contain behavior. In this study, we use the state diagrams and propose a mapping of its constructs to AspectJ language. We use the Reusable Aspect Models notation for this purpose. The approach addresses the mapping of both structure and behavior, however, owing to their strength with respect to modeling the system behavior, it essentially focuses on the state diagrams encapsulated in these models. A detailed mapping of different features of the state diagrams is proposed. The approach is illustrated by means of aspect models and corresponding mapped code from an aspect-oriented implementation of a Remote Service Caller example. The implementation shows that the approach can effectively be applied to obtain code for complete structure and behavior modeled using Reusable Aspect Models.
Implementing Symmetric Cryptography Using Sequence of Semi-Bent Functions

Symmetric cryptography is a cornerstone of everyday digital security, where two parties must share a common key to communicate. The most common primitives in symmetric cryptography are stream ciphers and block ciphers that guarantee confidentiality of communications and hash functions for integrity. Thus, for securing our everyday life communication, it is necessary to be convinced by the security level provided by all the symmetric-key cryptographic primitives. The most important part of a stream cipher is the key stream generator, which provides the overall security for stream ciphers. Nonlinear Boolean functions were preferred for a long time to construct the key stream generator. In order to resist several known attacks, many requirements have been proposed on the Boolean functions. Attacks against the cryptosystems have forced deep research on Boolean function to allow us a more secure encryption. In this work we describe all main requirements for constructing of cryptographically significant Boolean functions. Moreover, we provide a construction of Boolean functions (semi-bent Boolean functions) which can be used in the construction of orthogonal variable spreading factor codes used in code division multiple access (CDMA) systems as well as in certain cryptographic applications.
Perspectives on Becoming an Applied Machine Learning Scientist

While becoming a scientist is all about creating a dent in the boundaries of human knowledge, applying that expertise in a corporate setting requires a transformation of the scientist skill set. In both academia and industry, the primary focus is problem solving. In academia, however, it is targeted toward creating a novel solution that pushes the state of the art, while, in industry, the solution is valuable if it leads to the desired change in business metrics.
Automated Linear Book-Scanning System (ALBS)

The design of an automated linear book-scanning system (ALBS), with several attributes, is proposed in this work. For a start, the moving saddle by a stepper motor can hold up to 3 Kg weight and the system contains a suitably designed channel slot. Such a design is implemented as a new variation on the idea of pneumatic page turning. This involves the use of a vacuum cleaner device to pull book pages into a channel that flips them to the other side of the book, in order to allow two installed scanners to take an instant image of the two exposed pages, convert and successively merge them into a PDF file. The system is simple, economic and serves as a prototype for a consequent production line that would target various libraries and institutional sectors.
Effects of Substring Position and Character Similarity on Human Perception of Thai Herb Name Similarity

A name is usually used to identify persons and objects. Similarity between texts is useful for retrieving names regardless of misspelling and different spelling names. Moreover, similarity between names of health products may lead to safety issues for consumers. Although Levenshtein algorithm has been used for measuring similarities between a pair of strings, some factors may affect human perception. In this paper, effects of substring position and character similarity are taken into account. A set of experiments were done using Thai herb names collected in Thai herbal database. Similarity scores in percentage were given by six evaluators compared to the values provided by the original and modified Levenshtein algorithms. From the results, both factors have effects on human perception. For substring position, evaluators focused on substring portions between pairs of strings. When the same positions of substrings in a pair of strings are matched, more similarity scores should be given. For character similarity, groups of similar characters in Thai consonant letters are assigned the weight between 0 and 1 based on structure of Thai characters. Human perception responds to similarity on a pair of characters. The average similarity scores from evaluators were closer to our proposed Levenshtein algorithm with character similarity. In conclusion, similarities calculated from original Levenshtein algorithm should be adjusted based on substring position and character similarity.
A Novel Public Key Robust Watermarking Method for Still Images Based on Intentional Permutation Based on DCT and DWT

This article proposes a novel asymmetric watermarking method as an alternative to classical direct sequence spread spectrum for still images. In this new technique, the public key is a real sequence chosen according to a normal distribution with mean = 0 and variance = 1, the secret key is created by permuting intentionally elements in subsets of the public key. Watermark is combined linearly to public and secret key, is embedded into embedding location, it is extracted from host image after applying incorporating Discrete Cosine Transform (DCT) with discrete wavelet transform transformations (DWT). The experimental results prove that the proposed method is extremely more robust than the related work.
Efficient Software Implementation of Homomorphic Encryption for Addition and Multiplication Operations

Fully homomorphic encryption enables any type of calculation on encrypted data. There are several crypto libraries that provide such fully homomorphic encryption. However, since most libraries only support single level binary circuit operations, it is required for developers to efficiently implement basic arithmetic algorithms such as addition, subtraction, multiplication, and division for their own applications. In this paper, we propose fast binary addition and multiplication algorithms to support various bit-wise operations. To show the feasibility of the proposed algorithms, we implemented the proposed algorithms for 16, 32, 48, and 64 bits integers using the TFHE library. Our experiment results demonstrate that the proposed addition operation decreases the running time by 11 to 12%, and our multiplication implementation is about 3 to 4 times faster than the non-threaded method for 16, 32, 48 and 64 bits integers.
Proactive User Authentication Using WiFi Signals in Dynamic Networks

User authentication is the critical first step of network security to detect identity-based attacks and prevent subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply the cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic-based techniques grounded on physical layer properties to perform user authentication appears promising. To ensure the security of mobile devices in dynamic networks, we explore to use fine-grained channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform proactive user authentication. We propose a user-authentication framework that has the capability to proactively request CSI and build the user profile resilient to the presence of the spoofer. Our machine learning based user-authentication techniques can distinguish two users even when they possess similar signal fingerprints and detect the existence of the spoofer in dynamic network environments. Extensive experiments in both office and apartment environments show that our framework can remove the effect of signal outliers and achieve higher authentication accuracy compared to existing approaches that use received signal strength (RSS).
A Recommender System Based on Omni-Channel Customer Data

Recommender systems aim to provide personalized suggestions to customers which products to buy or services to consume. They can help to increase sales by helping customers discover new and relevant products. Traditionally, recommender systems use the purchase history of a customer, e.g., the purchased quantity or properties of the items. While this allows to build personalized recommendations, it is a very limited view of the problem. Nowadays, extensive information about customers and their personal preferences is available which goes far beyond their purchase behaviour. For example, customers reveal their preferences in social media, by their browsing habits and online search behaviour or their interest in specific newsletters. In this paper, we investigate how information from different sources and channels can be collected and incorporated into the recommendation process. We demonstrate this, based on a real-life case study of a retailer with several million transactions. We discuss how to employ a recommender system in this scenario, evaluate various recommendation strategies and describe how to incorporate information from different sources and channels, both internal and external. Our results show that the recommendations can be better tailored to the personal preferences of customers.
On Learning and Convergence of RBF Networks in Regression Estimation and Classification

In the paper we study convergence of the RBF networks with so-called regular radial kernels. The parameters of the network are learned by the empirical risk minimization. Mean square convergence of $$L_2$$ error is investigated using the machine learning tools such as VC dimension and covering numbers. RBF network estimates are applied in nonlinear function learning and classification.
Password Security in Organizations: User Attitudes and Behaviors Regarding Password Strength

Organizations and, to larger extent governments, have in recent years become more reliant than ever on the consistent operation of their information systems (ISs), which have become critical to their success and efficacy. Even though the growing dependence on ISs has created a pressing need for gathering information and easing its accessibility, security vulnerabilities in the systems have unfortunately spawned opportunities for hackers to compromise the system’s integrity and validity. This has put information systems confidentiality at high risk. As this paper shows, user authentication mechanisms, particularly passwords, determine confidentiality, which is an essential aspect of IS security. Passwords have been and are still a common method of securing ISs. A problem arises when users need to memorize and manage passwords that meet requirements, namely that users are more likely to use a single password across multiple accounts. User selection of passwords for use in ISs creates an impasse for the user. Furthermore, the systems progressive reliance on passwords for user authentication points to the necessity of investigating password creation policies in organizations. In spite of organizations recommendations on password choice to users, the majority do not choose to protect their passwords or even require choosing strong passwords in effect making hackers work easier. This study uses a survey questionnaire to evaluate user-generated password characteristics and present findings that prove user negligence with regards to password choice. Additionally, a literature review on password vulnerabilities in organizations is presented.
Application of Bayesian Methods in Disaster Risk Assessment

The disaster occurrences are irregular and random and the impact on the affected people are highly fluctuating. There occurs a surge of humanitarian assistance needs immediately after a disaster strikes within a short period. Traditional forecasting models are insufficient to estimate the risk occurrences and the relief aids demand. The proposed model predicts the probability of the future disaster occurrences and the magnitude of the impact conditional on the past casualties. We employ statistical identical parametrization to estimate the model parameters and update the final prediction based on the most recent disaster information using the Bayesian procedure. The outcome exhibits the propose forecasts performed well and better than a published result. The model would help humanitarian relief agencies to support pre-disaster planning and field adjustment to humanitarian resource allocations according to the emergency needs.