Reviewerly/exHarmony huggingface dataset:
An Integrated Recommender System Using Semantic Web With Social Tagging System

Social tagging systems (STSs) allow collaborative users to share and annotate many types of resources with descriptive and semantically meaningful information in freely chosen text labels. STS provides three recommendations such as tag, item and user recommendations. Existing recommendation algorithms transform the three dimensional space of user, resource, and tag into two dimensions using pair relations in order to apply existing techniques. However, users may have different interests for an item, and items may have multiple facets. To circumvent this, a new system that models three types of entities user, tag and item in a STS as a 3-order tensor is proposed. The sparsity is reduced using stemming and predictions are made by applying latent semantic indexing using randomized singular value decomposition (RSVD). The proposal provides all the three recommendations using semantic web and shows notable improvements in terms of effectiveness through indices such as recall, precision, time and space.
Empirical evaluation and proposals for bands-based COSMIC early estimation methods

Background. In the early phases of software development projects, thorough application of the COSMIC functional size measurement method may require more time and effort than available. Thus, early approximate methods have been proposed for estimating the COSMIC functional size of an application, instead of measuring it. Objective. The goal of this paper is to empirically evaluate the accuracy of the COSMIC early size estimation methods that are based on evaluations at the functional process level, for which historical data are available. The goal is to provide practitioners with empirical evidence on the accuracy of these methods. Method. We evaluated the Average Functional Process and the Equal Size Bands methods. We also proposed and evaluated two new approaches for defining bands in the Fixed Size Classification method. The estimation was performed by applying these methods to a set of software applications for which the data necessary to perform estimations were available, having been previously measured according to the standard COSMIC method. Results. Our analyses show that the Average Functional Process method generally provides estimates that are reasonable for early and quick sizing, but in some cases its estimation errors are too large to be acceptable. On the contrary, the methods using bands can provide quite accurate estimates. We determine the level of accuracy that can be obtained based on the type of method used, the number of bands used, and the quantitative characterization of the ability to classify each functional process in the correct band. Conclusions. The Average Functional Process method may be unreliable, as it occasionally yields quite large errors. Organizations using bands-based methods cannot just follow the prescribed estimation process: they need to properly train people in charge of classifying functional processes in the correct size band.
A New Chaotic Map Based Secure and Efficient Pseudo-Random Bit Sequence Generation

The security strength of symmetric encryption schemes rely on its internal source responsible for generation of efficient random encryption keys. A cryptographically strong encryption scheme needs a perfect mechanism that can generate statistically profound and secure pseudo-random sequences. To fulfill the requirement, we propose to present a novel pseudo-random number generation (PRNG) algorithm based on dynamical behaviour of a new and improved one-dimensional chaotic map. The dynamical characteristics of proposed chaotic map are analyzed through lyapunov exponents and bifurcation diagrams. The upright features of improved chaotic map are explored for synthesis of an efficient PRNG algorithm. The performance of proposed PRNG algorithm is examined using NIST SP800-22 and TestU01 randomness test suites, linear complexity, 0-1 balancedness, key-sensitivity, key space, etc. The randomness and other relevant statistical performance results of proposed PRNG algorithm demonstrate that it is consistent and suitable for its usage in cryptographic applications.
A Synthesis of Training Systems to Promote the Development of Engineering Competences

In the chapter, topical issues of development of the competence-based bilingual educational programs for training of specialists of an engineering profile, capable to solve at the international level complex scientific and technical challenges taking into account requirements of professional standards are considered. The special attention is paid to methodology of synthesis of training systems including virtual laboratories, computer simulators, and systems of imitating modeling for the practical-oriented training of specialists. The method of estimates of acquired professional competences on the basis of models of control of knowledge is offered, to implementation of scenarios and the analysis of protocols of training that allows to increase safety and efficiency of productions due to growth of qualification of personnel of industrial enterprises.
Punishment not Reward: Disincentivising Blockchain Application Misbehaviour

Blockchain architectures and applications emerged from the Bitcoin model, and are still most commonly associated with currency applications, and with financial speculation. This perception has driven the reward mechanisms for the various kinds of coin mining fueled consensus techniques seen in the vast majority of blockchain applications. As an alternative to reward mechanisms via coin payment, we proposedenial of service to the application in question and/orrevocation of participant confidentiality as punishment mechanisms for enterprise mission critical blockchain applications, to be used as part of the incentive mix sustaining the application. This obviates or diminishes the need for reward via cryptocurrencies, along with all their attendant volatility, insecure ecosystem and market manipulation demerits. We emphatically stress the importance of correctly balancing diverseapplication specific interests in the engineering of blockchain applications.
On the parallelization of Hirschberg's algorithm for multi‐core and many‐core systems

Summary Finding the longest common subsequence between two strings in acceptable time frames is crucial to solving various problems in different fields of study. To ensure the optimal solution is found, algorithms based on dynamic programming are employed almost exclusively. While the most commonly adopted algorithm, proposed by Needleman and Wunsch, has quadratic time and space complexity, the linear space complexity of Hirschberg's algorithm favors the comparisons of longer sequences. However, it too has a quadratic time complexity and therefore the effective exploitation of parallelism has become essential. This paper focuses on improving the execution efficiency of Hirschberg's algorithm on multi‐core and many‐core systems. To achieve this goal, first, enhancements to the sequential version are proposed to take advantage of SIMD instructions available on modern processors. Second, the impact on the performance of different parallelization strategies is investigated and evaluated. Results show that combining these two aspects can greatly improve the performance of Hirschberg's algorithm on these architectures. In relation to the original version, speedups of over 46 were achieved on a dual 18‐core server for sequences of 1.6 million characters. Furthermore, experiments with a 68‐core Intel Xeon Phi (many‐core) system obtained speedups of up to 105 for the same sequence size.
Social Design in Turkey through a Survey of Design Media: Projects, Objectives, Participation Approaches

The literature on social design consists of studies that report on single cases on the one hand and global reviews that are offered for theoretical purposes on the other. There is a lack of local reviews that report on social design practices that stem from peculiar political, economic, design professional and educational contexts. In response to this gap, we provide a review of 27 social design practices in Turkey from the last decade. The projects are compiled in accordance with social design criteria derived from the literature. Sampled projects were analysed via textual analysis of their representations on design media. In our findings, we demonstrate the ways in which local context shapes local social design practices. We also outline a framework for the discussion of prominent issues, range of actors, objectives, and participatory approaches.
Employing covert communications-based information reconciliation and multiple spatial modes to polarization entanglement QKD

The information that is leaked to an eavesdropper during the error reconciliation phase of quantum key distribution (QKD) protocols limits the maximum bit error rate (BER) of a system. In a standard QKD protocol, parity bits are transmitted over an authenticated noiseless channel, to which Eve has access. This Letter presents the concept of using a covert classical communication channel to transmit the parity bits between Alice and Bob without Eve gaining information of the transmitted parity bits. This allows for higher secure key generation rates and operation of a QKD system in which the BER exceeds the limit of the standard protocol. This concept is then applied to a practical free-space optical system that contains multiple parallel channels, where channel loss, crosstalk, atmospheric turbulence effects, and noise are considered.
Pseudotime Reconstruction Using TSCAN

In many single-cell RNA-seq (scRNA-seq) experiments, cells represent progressively changing states along a continuous biological process. A useful approach to analyzing data from such experiments is to computationally order cells based on their gradual transition of gene expression. The ordered cells can be viewed as samples drawn from a pseudo-temporal trajectory. Analyzing gene expression dynamics along the pseudotime provides a valuable tool for reconstructing the underlying biological process and generating biological insights. TSCAN is an R package to support in silico reconstruction of cells' pseudotime. This chapter introduces how to apply TSCAN to scRNA-seq data to perform pseudotime analysis.
Visual Analytics to Identify Temporal Patterns and Variability in Simulations from Cellular Automata

Cellular Automata (CA) are discrete simulation models, thus producing spatio-temporal data through experiments, as well as stochastic models, thus generating multi-run data. Identifying temporal patterns, such as cycles, is important to understand the behavior of the model. Assessing variability is also essential to estimate which parameter values may require more runs and what consensus emerges across simulation runs. However, these two tasks are currently arduous as the commonly employed slider-based visualizations offer little support to identify temporal trends or excessive model variability. In this article, we addressed these two tasks by developing, implementing, and evaluating a new visual analytics environment that uses several linked visualizations. Our empirical evaluation of the proposed environment assessed (i) whether modelers could identify temporal patterns and variability, (ii) how features of simulations impacted performances, and (iii) whether modelers can use the familiar slider-based visualization together with our new environment. Results shows that participants were confident on results obtained using our new environment. They were also able to accomplish the two target tasks without taking longer than they would with current solutions. Our qualitative analysis found that some participants saw value in switching between our proposed visualization and the commonly used slider-based version. In addition, we noted that errors were affected not only by the type of visualizations but also by specific features of the simulations. Future work may combine and adapt these visualizations depending on salient simulation parameters.
Recovering tampered regions in encrypted video using POB number system

Explosive growth in multimedia contents and wide spectrum of facilities provided by the cloud paradigm is attracting the global infrastructure towards it. However, the security and privacy issues of the outsourced content while availing these cloud based services is one of the major concerns. To secure the content, the traditional way is to render the data encrypted prior to outsourcing. However, just encrypting the data does not ensure that the content is intact as there may be attack attempts on the encrypted content. Hence, in this paper, an approach has been proposed to ensure the integrity of the content in the encrypted domain. If any breaches are attempted at the third party cloud service providers which alters the content in the encrypted form, the proposed scheme is able to first detect these tampered regions very accurately at the pixel level and thereafter, is capable of recovering the tampered content in the encrypted domain itself. Further, the overheads of bandwidth transmission are minimized here as the original information, spatial tampering bits, temporal tampering bits as well as the recovery information are compacted very efficiently exploiting the properties of the Permutation Ordered Binary (POB) number system. The scheme has been specifically tested with video contents as they contribute a major portion of the ever increasing multimedia repository. The efficacy of the scheme has been validated by testing against various attack scenarios and the scheme has been found to be performing satisfactorily well.
FashionSearchNet: Fashion Search with Attribute Manipulation

The focus of this paper is on retrieval of fashion images after manipulating attributes of the query images. This task is particularly useful in search scenarios where the user is interested in small variations of an image, i.e., replacing the mandarin collar with a buttondown. Keeping the desired attributes of the query image while manipulating its other attributes is a challenging problem which is accomplished by our proposed network called FashionSearchNet. FashionSearchNet is able to learn attribute specific representations by leveraging on weakly-supervised localization. The localization module is used to ignore the unrelated features of attributes in the feature map, thus improve the similarity learning. Experiments conducted on two recent fashion datasets show that FashionSearchNet outperforms the other state-of-the-art fashion search techniques.
Deep Sentiments Extraction for Consumer Products Using NLP-Based Technique

The growth in the field of e-commerce and product availability over the Internet is the higher availability of the consumable items is making the customers seek for higher quality and comparative price points. The primary reason for this ambiguity is the lack of in hand experience for the customers before the purchase. The customers mostly tend to rely on the feedbacks of the other buyers. The feedbacks on the products are often made in thousands in numbers, and it is difficult for the potential buyers to decide by looking into these feedbacks or reviews. Thus the demand of the modern research is to automate the process for extracting the true feedback matching their needs based on usage or price or location constraints. The feedback or the review system can be easily manipulated by the incorrect feedbacks. Hence it is important to reduce the influence of those feedbacks during extracting the overall sentiment of any product. Also, yet another challenge is that most of the feedbacks are not in formal English, thus making it difficult to extract the accurate feedback. This work proposes a novel-automated frame for extracting the deep sentiments from the reviews or the feedbacks on e-commerce websites. Another major outcome of this work is to detect the false reviews and making the sentiment true for any decision making. The research work generates a trustable sentiment extraction process to justify the need of true feedbacks for customer decision making.
Benchmarks and Models for Blockchain

In this presentation we consider blockchain from a performance engineering perspective, with an emphasis on consensus algorithms. A set of examples of performance characteristics and challenges of public blockchains serves as introduction to the presentation. These examples motivate a list of main topics that require further analysis by the research community, in both public and private blockchain variants. This list considers performance engineering challenges across the different layers of blockchain systems, which we identify as system, connector, and incentives layers, respectively. We go in some more depth regarding the evaluation of consensus algorithms, such as Proof of Work, which are a core element of the connector layer. In the presentation we will advocate probabilistic verification as a key approach to evaluate different consensus algorithms with respect to practically meaningful metrics. Throughout the talk, we present snippets of our recent research results in the area of modelling and benchmarking blockchain systems [1, 2, 3, 4, 5, 6].
Tele-Media-Art:Feasibility Tests of Web-Based Dance Education for the Blind Using Kinect and Sound Synthesis of Motion

Tele-media-art is a web-based asynchronous e-learning platform, enabling blind students to have dance and theatre classes remotely, using low-cost motion tracking technology feasible for home use. Teachers and students submit dance recordings augmented with sound synthesis of their motions. Sound synthesis is generated by processing Kinect motion capture data, enabling blind students to compare the audio feedback of their motions with the audio generated by the teacher's motions. To study the feasibility of this approach, the authors present data on early testing of the prototype, performed with blindfolded users.
Learning English–Chinese bilingual word representations from sentence-aligned parallel corpus

Representation of words in different languages is fundamental for various cross-lingual applications. In the past researches, there was an argument in using or not using word alignment in learning bilingual word representations. This paper presents a comprehensive empirical study on the uses of parallel corpus to learn the word representations in the embedding space. Various non-alignment and alignment approaches are explored to formulate the contexts for Skip-gram modeling. In the approaches without word alignment, concatenating A and B, concatenating B and A, interleaving A with B, shuffling A and B, and using A and B separately are considered, where A and B denote parallel sentences in two languages. In the approaches with word alignment, three word alignment tools, including GIZA++, TsinghuaAligner, and fast_align, are employed to align words in sentences A and B. The effects of alignment direction from A to B or from B to A are also discussed. To deal with the unaligned words in the word alignment approach, two alternatives, using the words aligned with their immediate neighbors and using the words in the interleaving approach, are explored. We evaluate the performance of the adopted approaches in four tasks, including bilingual dictionary induction, cross-lingual information retrieval, cross-lingual analogy reasoning, and cross-lingual word semantic relatedness. These tasks cover the issues of translation, reasoning, and information access. Experimental results show the word alignment approach with conditional interleaving achieves the best performance in most of the tasks.
Nonuniform families of polynomial-size quantum finite automata and quantum logarithmic-space computation with polynomial-size advice

The state complexity of a finite(-state) automaton intuitively measures the size of the description of the automaton. Sakoda and Sipser [STOC 1972, pp. 275–286] were concerned with nonuniform families of finite automata and they discussed the behaviors of the nonuniform complexity classes defined by such families of finite automata having polynomial-size state complexity. In a similar fashion, we introduce state complexity classes using nonuniform families of quantum finite automata empowered by the flexible use of garbage tapes. We first present general inclusion and separation relationships among state complexity classes of nonuniform families of various one-way finite automata, including deterministic, nondeterministic, probabilistic, and quantum finite automata having polynomially many inner states. For two-way quantum finite automata equipped with flexible garbage tapes, we show a close relationship between the state complexity of the nonuniform family of such polynomial-size quantum finite automata and the parameterized complexity class induced by logarithmic-space quantum computation assisted by polynomial-size advice. We further establish a direct connection between space-bounded quantum computation with quantum advice and quantum finite automata whose transitions are dictated by superpositions of transition tables.
Factoring semi-primes with (quantum) SAT-solvers

The computational difficulty of factoring large integers forms the basis of security for RSA public-key cryptography. The best-known factoring algorithms for classical computers run in sub-exponential time. The integer factorization problem can be reduced to the Boolean Satisfiability problem (SAT). While this reduction has proved to be useful for studying SAT solvers, large integers have not been factored via such a reduction. Shor's quantum factoring algorithm factors integers in expected polynomial time. Large-scale fault-tolerant quantum computers capable of implementing Shor's algorithm are not yet available, preventing relevant benchmarking experiments. Recently, several authors have attempted quantum factorizations via reductions to SAT or similar NP-hard problems. While this approach may shed light on algorithmic approaches for quantum solutions to NP-hard problems, in this paper we study and question its practicality. We find no evidence that this is a viable path toward factoring large numbers, even for scalable fault-tolerant quantum computers, as well as for various quantum annealing or other special purpose quantum hardware.
Robust 3D Pig Measurement in Pig Farm

On a pig farm, the shipment of pigs of proper weight is very important for increasing profit. However, in order to reduce labor costs, many farmers ship pigs without weighing them. Therefore, an automatic sorting system that selects pigs that have reached the proper weight by measuring the weight of each pig has been developed. In the present paper, a weight estimation system using a camera for pig sorting is introduced. Three-dimensional visual information on a pig captured in a single image is used to estimate its weight. The proposed method is robust and practical for the measurement of a moving animal in a poor environment of pig farms.