Reviewerly/exHarmony huggingface dataset:
Dependency Based Bilingual word Embeddings without word alignment

In this work, we trained different bilingual word embeddings models without word alignments (BilBOWA) using linear Bag-of-words contexts and dependency-based contexts. BilBOWA embedding models learn distributed representations of words by jointly optimizing a monolingual and a bilingual objective. Including dependency features in the monolingual objective, improves the accuracy of learning bilingual word embeddings up to 6% points in English-Spanish (En-Es) and up to 2.5% points in English-German (En-De) language pairs in word translation task compared to the baseline model. However, using these dependency features in both monolingual and bilingual objectives does not lead to any improvement in the En-Es language pair and only shows minor improvement for En-De. Moreover, our results provide evidence that using dependency features in bilingual word embeddings has a different effect based on syntactic and sentence structure similarity of the language pair.
Exploring the Use of Genetic Algorithm Clustering for Mobile App Categorisation

Search-based approaches have been successfully used as clustering algorithms in several domains. However, little research has looked into their effectiveness for clustering tasks commonly faced in Software Engineering (SE). This short replication paper presents a preliminary investigation on the use of Genetic Algorithm (GA) to the problem of mobile application categorisation. Our results show the feasibility of GA-based clustering for this task, which we hope will foster new avenues for Search-Based Software Engineering (SBSE) research in this area.
Secure Matrix Computation: A Viable Alternative to Record Linkage?

Linking data from different sources can enrich the research opportunities in the Social Sciences. However, datasets can typically only be linked if the respondents consent to the linkage. Strategies from the secure multi-party computation literature, which do not require linkage on the record level, might be a viable alternative in this context to avoid biases due to selective non-consent. In this paper, we evaluate whether such a strategy could actually be successfully applied in practice by replicating a study based on linked data available at the Institute for Employment Research. We find that almost identical results could be obtained without the requirement to link the data. However, we also identify several problems suggesting that the proposed strategy might not be feasible in many practical contexts without further modification.
Restoration of Digital Design Using Row and Column Major Parsing a Technique

We present a novel design and development of a photo electronics-based system for reading old and used punched cards and storing its binary information for transforming them into an effective image file format. The punched card's holes diameters having the sizes of 3mm, 5mm, and 5.5mm pitch. Before the adaptation of computing systems in the field of many industries, those punched cards were prepared manually without digital design sources, but those punched cards are having rich woven designs. Now, the idea is to retrieve binary information from the punched cards and store them in digital (Non-Graphics) format before processing it. After processing the digital format (Non-Graphics) it is converted into an effective image file format through either by Row major or Column major parsing technique. To accomplish these activities, an embedded system based device and software integration are developed.
Designing Palpable Data Representations

This paper discusses a multisensory approach to data representation with a specific focus on haptic media. In this, I provide a philosophical and methodological overview of my design process informed by the following themes and topics: 1) the haptic subject; 2) touch as political; 3) co-formed knowledge; and 4) arts-based research methods. The overview is further contextualized by a thorough analysis of collaborative work Vibrant Lives, a 4-year project that includes a suite of unique, custom-designed, vibrotactile interfaces that give audiences a real-time experience of their own personal data output. I continue my analysis by sharing observations from a series of workshops I conducted with haptified archive data. In conclusion, I reflect on issues of user ethics, agency, and control when designing touch-based experiences of data in a multisensory installation setting.
Power Aware GALS Based Pipelined DES System

The method of cryptography establishes the confidentiality for digital data transmission and storage. Cryptography is used in different applications like ecommerce, health-monitoring and military for protecting information. The Data encryption standard (DES) is widely used cryptographic algorithm due to its symmetric nature and economical implementation to provide short term data security. The same key and architecture set is used for encrypting and decrypting in DES algorithm accordingly. The substitution operation is performed by S-Box by using the key to obtain information uniquely. This paper implements DES algorithm using Globally Asynchronous Locally Synchronous (GALS) methodology which uses independent clock. This improves speed due to its pipelined architecture enabling concurrent data processing. The encryption and decryption engine are implemented by VLSI architecture. This architecture is simulated in Verilog HDL and synthesized on the Xilinx 14.2 device. The logical units of architecture on Field Programmable Gate Array (FPGA) increases by 6.25% to provide improved security. Hence, the speed is improved by trading off with area. This proposed method is proved to be robust for Differential Power Analysis (DPA) which is analyzed from the statistics of the processing time through 50000 encryptions.
Bathing in lightness

Bathing in Lightness is an interactive light and sound installation that seems to be enlivened by a swarm entity trying to explore its inner world and communicate with the outer one. Consisting of 52 filament light bulbs it visualizes the movement of a particle swarm that is driven by the presence of the viewer and its own inner urge. Visitors can interact with the installation by moving in front of it while their movement is followed by the swarm and thus being translated into light and sound - visible inside the cluster of light bulbs and audible via nearby speakers or headphones.
Graph Pattern Loss Based Diversified Attention Network For Cross-Modal Retrieval

Cross-modal retrieval aims to enable flexible retrieval experience by combining multimedia data such as image, video, text, and audio. One core of unsupervised approaches is to dig the correlations among different object representations to complete satisfied retrieval performance without requiring expensive labels. In this paper, we propose a Graph Pattern Loss based Diversified Attention Network (GPLDAN) for unsupervised cross-modal retrieval to deeply analyze correlations among representations. First, we propose a diversified attention feature projector by considering the interaction between different representations to generate multiple representations of an instance. Then, we design a novel graph pattern loss to explore the correlations among different representations, in this graph all possible distances between different representations are considered. In addition, a modality classifier is added to explicitly declare the corresponding modalities of features before fusion and guide the network to enhance discrimination ability. We test GPLDAN on four public datasets. Compared with the state-of-the-art cross-modal retrieval methods, the experimental results demonstrate the performance and competitiveness of GPLDAN.
Improve the LSTM and GRU model for small training data by wavelet transformation

Regarding collision prediction technology, the most common are car reversing radar and infrared rays, which provide warnings by sensing the distance between objects and cars. Although radar detection is accurate, radar systems that can provide instant 360-degree feedback are very expensive. The cost of infrared is much lower, but they cannot be applied to high-temperature environments. As the result, the technology of preventing collisions using only images from cameras is an important artificial intelligence topic in recent years. If a low-resolution CCD and artificial intelligence technology can be used to achieve a certain degree of accuracy in collision prediction, then low-cost anti-collision technology is worth looking forward to. Furthermore, we hope to provide anti-collision warnings on motorcycles and bicycles using this technology. In order to achieve this goal, computational simplification is a technical threshold. It's only when simple calculations achieve high-precision prediction can we meet the low power consumption requirements for image AI to be applied small vehicles. Therefore, we hope to find out a better image representation basis and combine it with AI technology to fulfill the requirements of less calculation and high accuracy. In addition, we also hope to create models with sufficient accuracy with small training data. This experiment will reduce development costs and get better efficiency in the early stage of developing ADAS.
Data Security Using Matrix Operations

In today’s world internet is being widely used for electronic information exchange in one form or the other to the specific receiver using some medium. Secure communication is of major concern while data is being transmitted over the internet. Data security is the study of various methods of protecting data in computer and communication systems from unauthorized disclosure and modification. Security of data can be achieved by a technique called cryptography. This paper attempts to propose a new cipher model using block cipher concept. A matrix is used to store the data. In order to make the data more secure and inaccessible to unauthorized users, various matrix operations can be carried out on the data matrix. Also, a slightly different method is mentioned to share the secret key used during the encryption and decryption mechanisms. This proposed new cipher model enables good diffusion for making a secure cipher.
Automatic Handwriting Inference via Motion Sensor Embedded Wrist-worn Device

The rise of sensor-equipped Internet of Things devices has flourished the development of smart sensing systems. Handwriting sensing as an important enabler for applications in smart homes and smart educations, etc, is a challenging problem due to the noise in the sensor data collected from common off-the-shelf MEMS equipped devices, as well as the diversities in the writing style of different users. In this paper, to encounter these problems, we propose a novel smartwatch based handwriting sensing system which is unobtrusive to use and achieves high recognition accuracy. To achieve a robust recognition accuracy, we incorporate a data pre-processing module and a novel feature fusion model which employs PCA for time domain and frequency domain feature fusion. A deep learning model is then used to achieve a high recognition performance with 92.42% accuracy on average. The results show that the system has potential to support future smart handwriting sensing applications.
Automatic Positioning of Hippocampus Deformable Mesh Models in Brain MR Images Using a Weighted 3D-SIFT Technique

Automatic hippocampus segmentation in Magnetic Resonance (MR) images is an essential step in systems for early diagnostic and monitoring treatment of Alzheimer’s disease (AD). It allows quantification of the hippocampi volume and assessment of their progressive shrinkage, considered as the hallmark symptom of AD. Among several methods published in the literature for hippocampus segmentation, those using anatomical atlases and deformable mesh models are the most promising ones. Although these techniques are convenient ways to embed the shape of the models in the segmentation process, their success greatly depend on the initial positioning of the models. In this work, we propose a new keypoint deformable registration technique that uses a modification of the 3D Scale-Invariant Feature Transform (3D-SIFT) and a keypoint weighting strategy for automatic positioning of hippocampus deformable meshes in brain MR images. Using the Mann-Whitney U test to assess the results statistically, our method showed an average improvement of 11% over the exclusive use of Affine transformation, 30% over the original 3D-SIFT and 7% over the non-weighted point procedure.
Davidson’s Triangulation Argument in the Logic of Actions

In this chapter, I provide a formal machinery to complement the epistemic background in Davidson’s triangulation argument by presenting a structural modelling of the argument in the framework of an epistemic logic of actions. I focus on two aspects. Firstly, to guarantee that the propositional content of what the speaker says in the ongoing communication is justified true beliefs, I suggest that the speaker’s utterances must be assertions governed by the knowledge norm, assuming Davidson’s characterization of knowledge in terms of holistically justified true beliefs. Secondly, I claim that ongoing communication succeeds only if what the speaker says becomes common knowledge. Accordingly, the required underlying system for the proposed logic of actions will be a logic of common knowledge, in which the notion of common knowledge is characterized in terms of the knowledge account of assertion. A semantics appropriate for the logic of common knowledge will be given. I further propose that the transition from the speaker’s initial assertion to common knowledge can be illuminated by virtue of individual agents’ learning and the group learning, resulting from what the hearer learns from the speaker’s assertion and what the speaker learns from the hearer’s response, and the following interactions between them. Two act-operators for individual agents’ learning and group learning are introduced; the required semantic rules are stipulated.
Nonclassicality Used as Quantum Information Processing in Nonlinear Optical Systems

We have studied nonclassical effects in pump mode in seven-wave mixing nonlinear process involving pump photons of different frequencies. The nonclassicality of a system increases with increase in the number of photons present in the system prior to interaction in nonlinear medium. We have found squeezing and sub-Poissonian photon statistics that can be used in obtaining noise reduction in signal processing. Further, we have also obtained antibunching of photons that can be used as a criteria of obtaining single-photon source that is primary requirement for quantum information processing.
Adaptive dialogue management using intent clustering and fuzzy rules

Abstract Conversational systems have become an element of everyday life for billions of users who use speech‐based interfaces to services, engage with personal digital assistants on smartphones, social media chatbots, or smart speakers. One of the most complex tasks in the development of these systems is to design the dialogue model, the logic that provided a user input selects the next answer. The dialogue model must also consider mechanisms to adapt the response of the system and the interaction style according to different groups and user profiles. Rule‐based systems are difficult to adapt to phenomena that were not taken into consideration at design‐time. However, many of the systems that are commercially available are based on rules, and so are the most widespread tools for the development of chatbots and speech interfaces. In this article, we present a proposal to: (a) automatically generate the dialogue rules from a dialogue corpus through the use of evolving algorithms, (b) adapt the rules according to the detected user intention. We have evaluated our proposal with several conversational systems of different application domains, from which our approach provided an efficient way for adapting a set of dialogue rules considering user utterance clusters.
Structured (De)composable Representations Trained with Neural Networks

Abstract The paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing to decompose the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data).
A Novel Approach for Detection of Basketball Using CFD Method

Video analysis plays a vital role in detecting and tracking an object. In a dynamic environment, such as sports or military area, it is difficult to detect and track specified object. In this paper, we have presented a novel algorithm to detect a basketball in the court. The objects are detected from video frames using different types of realms like HSV space, time, frequency, histogram, etc. For ball detection, Color Based and Frame Differencing (CFD) method is proposed and experimented. The complexities arise in each stage such as preprocessing, segmentation, feature extraction, background subtraction, and hole filling. Color quantization is applied to minimize the number of distinct colors in the video. Frame differencing method utilizes the difference in components between frames for ball detection. Morphological operations on connected components are applied to increase the performance and reduce the complexities.
Representation of Information and Satisfiability, Algorithms for Multi-Agent Logics

This paper studies logical properties of multi-agent logics based at relational models with elements of multi-agency. In particular, we introduce a multi-agent temporal logic PastL based at linear possible-worlds models with non-transitive and non-uniform accessibility relations. We consider the time directed to past, and use logical operation Since for non-transitive accessibility; Several formalizations for the conception knowledge/agent’s knowledge in terms of non-transitive time in past are suggested. A logics PastT based is this approach is studied, we consider main computational problems - satisfiability and decidability. Based at technique of reduced normal forms we solve these problems and find deciding algorithms. Several interesting open problems are suggested. In final part of the paper we extend obtained results on logics with different accessibility relations for different agents and consider the satisfiability problem for these logics.
Useful Organizational Knowledge via Knowledge Elicitation and Management

Currently, and with almost light speed, new advances in both human-centered and technology- driven efforts have led to new methods in obtaining and using organizational knowledge. For the purposes of this Chapter, Organizational Knowledge will be seen as the different knowledge and skill sets that the employees of a large company or organization have, and how these can be used and shared to make the organization more effective. The Chapter's emphasis will be on how to elicit those knowledges and skill sets, then how to both use and evaluate their usage. The author's goal is to explicate a new model for eliciting and using Organizational Knowledge. This new paradigm uses many components in a blended Delphi. Advantages of using these components are that they are all content-area neutral and, in a real sense, generic in application. Collectively, the resultant paradigm for developing and using organizational knowledge points the way to try the new procedures and validate (or not) them and, to future research on an organizational level.
Design and Implementation of Parameters-Based 3D Cutting Function for Dynamic Geometry Software

The cutting of three-dimensional (3D) geometric shapes is an important issue in solid geometry, and it has indispensable functional requirements for 3D Dynamic Geometry Software (DGS). There are some problems of cumbersome steps and poor experimental effect when constructing the cutting of 3D geometric shapes in many existing 3D-DGS. The parameters-based 3D cutting method for DGS is presented in this paper. Firstly, we build a core model of the parameters-based 3D cutting function. Secondly, when processing the core calculation of the cutting function, we mainly calculate the relevant values of 3D geometric shape and cutting planes in two cases. One is parallel cutting, and the other is series cutting. Finally, an experimental result show that the continuous change of the cut shapes, greatly optimizing the cutting affect, and simplifying the construction steps of 3D cutting can be realized by the 3D cutting function designed in the paper. At present, more and more people use 3D cutting function to create intuitive and vivid dynamic geometry resources.
Method for Validating Graph Models Based on the Effective Control Algorithm

The article proposes a method for validating mathematical models represented by oriented weighted signed graphs using an efficient control algorithm. The method considers the validated model in terms of spectral properties of the graph adjacency matrix represented by a fuzzy cognitive map (FCM). Using an efficient control algorithm, you can determine the eigenvector direction of the adjacency matrix. This property defines the criteria for checking the FCM.
Mathematical Spatial Minerals Distributing Model by Interlineation Methods of Matrix-functions

The paper considers the problem of restoration at each point between the given well system (inclined line) of periodic table elements of finite sets or their linear density compounds at the given depth. The restrictions are imposed by n-selected elements or their compounds. Method for the construction of interlineation operator of matrix functions is proposed, each component of which depends on three variables on the system of curves, coinciding with approximate matrix function at all points at the given depth and makes the calculation of this matrix function value possible at each point between the wells.
Laplacian-based Semi-supervised Multi-Label Regression

In multi-label learning, one has to find a model suitable for predicting multiple values for the same individual, based on the same features. The effectiveness of most multi-label algorithms lies in the fact that it is able to consider the correlations between the related labels. On the other hand, in many applications, a multi-label learning task incurs a high cost for the annotation of a single data point. This leads to a dataset consisting of a few labeled data points, and many more unlabeled data points. In this scenario, semi-supervised methods can take advantage of the unlabeled data points. In this article, we propose a new algorithm for multi-label semi-supervised regression, LSMR, as a multi-label extension of a semi-supervised regression algorithm. We provide experimental results on some publicly-available regression datasets showing the effectiveness of our approach.