Reviewerly/exHarmony huggingface dataset:
SPCSS: Social Network Based Privacy-Preserving Criminal Suspects Sensing

With development of online social networks, many criminal suspects use social network to communicate with each other. In order to obtain valuable criminal clues, considerable research works have been done to analyze criminal suspects' social data. However, most of them did not pay much attention on privacy-preserving problems, which may leak some sensitive data in the analysis process. To solve this problem, we propose a novel analysis approach of criminal suspects by exploiting social data and crime data that are collected by social network and police information systems. We enable the social cloud server and public security cloud server to exchange social information of criminal suspects and user's public information in a privacy-preserving way. Specifically, we propose a privacy-preserving data retrieving method based on oblivious transfer to guarantee that only the authorized entities can perform queries on suspects' social data, while the social cloud server cannot infer anything during the query. Moreover, several building blocks, such as encrypted data comparing, secure classification and regression tree (CART) model are also proposed. Based on these building blocks, we designed a privacy-preserving criminal suspects sensing scheme. Finally, we demonstrate a performance evaluation which shows that our scheme can enhance analysis of criminal suspects without privacy leakage, while with low overhead.
Performance Comparison of Different Classification Algorithms for Household Poverty Classification

There should be a way to identify poor households as the potential receiver of poverty alleviation programs if the aim of an organization is to serve the deprive one. This problem is addressed by using machine learning algorithms particularly the classification algorithms. In this study, data mining was used to analyze the poverty data that was extracted from the Community Based Monitoring System (CBMS) Database of Lagangilang, Abra, Philippines. Different methods of data mining classification were evaluated and compared like Naïve Bayes, ID3, Decision Tree, Logistic Regression and KNearest Neighbor (KNN). In order to get the best predictive model, the different classification algorithms were evaluated based on the performance metrics; accuracy, precision, recall, F1 score and AUC. The rapid miner was the datamining tool used to process the different classification algorithms and to analyze the dataset. The study showed that Naïve Bayes classifier is an efficient algorithm for predicting households that are poor and non-poor because it outperformed all the four algorithms in all the performance metrics used in the study. Furthermore, the error rate of the said algorithm is significant because it is 0.0014 only. Therefore, choosing the best classification algorithm for data mining task will give a better result.
The pause/play button actor-network: lecture capture recordings and (re)configuring multi-spatial learning practices

Lecture recording is an increasingly common practice in UK universities, whereby audio, video, and multimedia content from lecture theatres can be captured and distributed online. Despite a large body of recent lecture capture literature, much of the empirical research adopts positivist paradigms, which overlooks the complex and unpredictable nature of teaching and learning. Addressing this knowledge gap, this exploratory case study adopts sociomaterial approaches, specifically perspectives from the domain of actor-network theory (ANT), to view learning technologies as complex assemblages involving heterogeneous human and non-human entities or actors.This paper explores the entanglements involved in enacting online pedagogy and learning across spatiotemporal dimensions using trace ethnography and visualisation mapping. Examining the student-led study practices revealed that multitasking and fluid task switching, between contrasting networks and spaces, was a significant activity during the playback of lecture recordings. Exploring an innocuous and ubiquitous practice, such as video pausing, affords nuanced perspectives into the sociomaterial entanglements involved in enacting study practices. Moreover, adopting multimodal sensitivities reveals how often overlooked modes, such as iconography, can become actors within an assemblage. This may offer new insights into how modes help produce or stabilise configurations and advance efforts in attending to the non-human within actor-networks.
Code-Mixed to Monolingual Translation Framework

The use of multilingualism among the new generation is widespread in the form of code-mixed data on social media, and therefore a robust translation system is required for catering to the novice and monolingual users. In this work, we present a translation framework that uses a translation-transliteration strategy for translating code-mixed data into their equivalent monolingual instances. One of the goals of this work is to translate a code-mixed source (written in Roman script) to a Bengali target (written in Devanagari script), where the source may contain English, along with transliterated Bengali. Finally, to convert the output to a more readable form, it is reordered using a target language model. The decisive advantage of the proposed framework is that it does not require a code-mixed to monolingual parallel corpus for training and decoding. On testing the framework, it achieved BLEU and TER scores of 16.47 and 55.45, respectively. Since the proposed framework deals with various sub-modules, we dive deeper into the importance of each of them, analyze the errors and finally, discuss some improvement strategies.
Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization

This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO) for the multi-dimensional numerical problems. The best practices of ABC and TLBO are implemented to provide robust algorithms on a distributed memory computation environment using MPI libraries. Island parallel versions of the proposed hybrid algorithm are observed to obtain much better results than those of sequential versions. Parallel pseudorandom number generators are used to provide diverse solution candidates to prevent stagnation into local optima. The performances of the proposed hybrid algorithms are compared with eight different metaheuristics algorithms of particle swarm optimization, differential evolution variants, ABC variants and evolutionary algorithm. The empirical results show that the new hybrid parallel algorithms are scalable and the best performing algorithms when compared to the state-of-the-art metaheuristics.
Color Filter Arrays for Quanta Image Sensors

Quanta image sensor (QIS) is a new type of image sensors with single-photon sensitivity. However, the existing literature on QIS has been focusing on monochromatic signals. In this article, we discuss how color imaging can be made possible by designing color filter arrays for QIS and other small pixels. Designing color filter arrays for small pixels is challenging because maximizing the light efficiency while suppressing aliasing and crosstalk are conflicting tasks. We present an optimization-based framework which unifies several mainstream color filter array design methodologies. Our method offers greater generality and flexibility. Compared to existing methods, the new framework can simultaneously handle luminance sensitivity, chrominance sensitivity, cross-talk, anti-aliasing, manufacturability and orthogonality. Extensive experimental comparisons demonstrate the effectiveness of the framework.
Truth, Proof, and Reproducibility: There’s No Counter-Attack for the Codeless

Current concerns about reproducibility in many research communities can be traced back to a high value placed on empirical reproducibility of the physical details of scientific experiments and observations. For example, the detailed descriptions by 17th century scientist Robert Boyle of his vacuum pump experiments are often held to be the ideal of reproducibility as a cornerstone of scientific practice. Victoria Stodden has claimed that the computer is an analog for Boyle’s pump – another kind of scientific instrument that needs detailed descriptions of how it generates results. In the place of Boyle’s hand-written notes, we now expect code in open source programming languages to be available to enable others to reproduce and extend computational experiments. In this paper we show that there is another genealogy for reproducibility, starting at least from Euclid, in the production of proofs in mathematics. Proofs have a distinctive quality of being necessarily reproducible, and are the cornerstone of mathematical science. However, the task of the modern mathematical scientist has drifted from that of blackboard rhetorician, where the craft of proof reigned, to a scientific workflow that now more closely resembles that of an experimental scientist. So, what is proof in modern mathematics? And, if proof is unattainable in other fields, what is due scientific diligence in a computational experimental environment? How do we measure truth in the context of uncertainty? Adopting a manner of Lakatosian conversant conjecture between two mathematicians, we examine how proof informs our practice of computational statistical inquiry. We propose that a reorientation of mathematical science is necessary so that its reproducibility can be readily assessed.
Motion path planning of 6-DOF industrial robot based on fuzzy control algorithm

The fuzzy control algorithm is used to establish the relation between the functions of motion of 6-DOF (degree of freedom) industrial robots on the rotation angle of each joint. The algorithm optimizes the motion path of the robot, thereby accelerating the increase in industrial productivity and pr omoting the development of industrial production. During the motions, the 6-DOF industrial robots have weak avoidance ability toward the encountered obstacles, which is not conducive to the safe production and will reduce industrial efficiency. Therefore, by analyzing and summarizing the previous researches, the fuzzy control algorithm is used to construct and optimize the kinematics model, thereby proposing a method of robot motion path planning. Also, based on the unstructured operating environment, a multi-functional motion navigation system for 6-DOF industrial robots is proposed. The experimental results show that the fuzzy control algorithm can optimize the robot motion path, shorten the time of motion, and make the robots reach the destinations smoothly. The algorithm can avoid safety accidents in industrial production effectively, reduce casualties, improve industrial productivity, and promote the optimized allocation of human resources. The motion system of 6-DOF industrial robot based on fuzzy control algorithm has excellent practicability, which can promote the development of industrial production effectively and be widely applied to industrial production.
Dart: Divide and Specialize for Fast Response to Congestion in RDMA-Based Datacenter Networks

Though Remote Direct Memory Access (RDMA) promises to reduce datacenter network latencies significantly compared to TCP (e.g., 10x), end-to-end congestion control in the presence of incasts is a challenge. Targeting the full generality of the congestion problem, previous schemes rely on slow, iterative convergence to the appropriate sending rates (e.g., TIMELY takes 50 RTTs). Several papers have shown that even in oversubscribed datacenter networks most congestion occurs at the receiver. Accordingly, we propose a divide-and-specialize approach, called Dart, which isolates the common case of receiver congestion and further subdivides the remaining in-network congestion into the simpler spatially-localized and the harder spatially-dispersed cases. For receiver congestion, we propose direct apportioning of sending rates (DASR) in which a receiver for n senders directs each sender to cut its rate by a factor of n, converging in only one RTT. For the spatially-localized case, Dart provides fast (under one RTT) response by adding novel switch hardware for in-order flow deflection (IOFD) because RDMA disallows packet reordering on which previous load balancing schemes rely. For the uncommon spatially-dispersed case, Dart falls back to DCQCN. Small-scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60% (2.5x) and 79% (4.8x) lower 99t'-percentile latency, and similar and 58% higher throughput than InfiniBand, and TIMELY and DCQCN.
Deep Scalable Image Compression via Hierarchical Feature Decorrelation

Scalable image compression allows reconstructing complete images through partially decoding. It plays an important role for image transmission and storage. In this paper, we study the problem of feature decorrelation for Deep Neural Network (DNN) based image codec. Inspired by self-attention mechanism [1], we design a transformer-based decorrelation unit (DU) and adopt it in our scalable image compression framework to reduce the redundancy of feature representations at different levels. Experimental results demonstrate that proposed framework outperforms the state-of-the-art DNN-based scalable image codec and conventional scalable image codecs in terms of MS-SSIM. We also conduct ablation experiments which explicitly verify the effectiveness of decorrelation unit in our scheme.
Mathematical model for calculating solar radiation on horizontal and inclined surfaces for the conditions of Yakutsk

Abstract The purpose of this research is to develop a simple calculation technique for solar flux density on horizontal and inclined surfaces to allow estimation of the amount of heat energy entering a hybrid system of solar heat supply at any time. Based on the suggested method corresponding software is developed for defining solar flux on horizontal and inclined surfaces using measurement data of maximum daily solar radiation. Hourly values of total and diffuse solar radiation on horizontal surface are defined, as well as average daily, monthly and annual values of total solar radiation. The obtained calculation results are compared to the existing reference, experimental and calculation data on total solar radiation falling on horizontal surface in the city of Yakutsk. The calculated solar flux values show sufficient consistency with the reference materials and NASA SSE database. The paper provides description of the method for calculating specific solar heat flux on inclined surface with known heat fluxes of direct and diffuse solar radiation falling on horizontal surface.
Recurrent convolutions of binary-constraint Cellular Neural Network for texture recognition

Texture recognition is one of the most important branches in image research. This paper mainly aims to develop a new solution to address texture recognition using a Cellular Neural Network (CellNN). Firstly, it proposes an improved model of CellNN by the binary constraints of local receptive fields, and then designs a recurrent convolution framework of such a model to generate two types of texture feature maps, including state feature maps and output feature maps. In order to obtain low-dimensional features, state feature maps are further compressed by the mapping of rotation-invariant patterns and the merging of low-frequency occurrence patterns. By the statistics of joint-distribution patterns, state feature maps and output feature maps are fused together to generate the features of single resolution. Moreover, a multi-resolution feature combination scheme is also designed by the optimization of softmax & variance and concatenation of multiple features. Finally, a fully-connected neural network is trained to work as a texture recognizer. The experimental comparisons of totally 15 algorithms on five benchmark datasets show that, on the dataset whose texture-class quantity is not beyond 30, such as Brodatz, our method could always acquire the highest recognition accuracy, outperforming any other compared ones. On the big dataset with huge texture-class quantity, such as ALOT, our method could also surpass any other non-deep-learning one, such as the state-of-the-art gLBP, only slightly falling behind the best two deep-learning ones, FV-Alex and FV-VGGVD. However, in terms of time cost, our method could always outperform any deep-learning one in feature extraction stage, and also surpass any compared one except original LBP in feature matching.
Joint Reflection Removal and Depth Estimation From a Single Image

Reflection caused by glass often degrades the quality of an image and further makes it difficult to estimate depth. In this article, we propose joint reflection removal and depth estimation from a single image. We perform reflection removal (transmission recovery) and depth estimation jointly using a collaborative neural network that consists of four blocks: 1) encoder for feature extraction; 2) reflection removal subnetwork (RRN); 3) depth estimation subnetwork (DEN); and 4) depth refinement guided by the transmission layer. We achieve collaboration between reflection removal and depth estimation by concatenating intermediate features of DEN with RRN. Since the recovered transmission layer contains accurate edges of objects behind glass, we refine the estimated depth with its guidance by guided image filtering. The experimental results demonstrate that the proposed method achieves both reflection removal and depth estimation even for images with dominant reflections. Besides, this article offers a new way of treating reflections in images to introduce depth estimation into reflection removal and achieve reflection removal and depth estimation simultaneously.
Building interactive sentence-aware representation based on generative language model for community question answering

Semantic matching between question and answer sentences involves recognizing whether a candidate answer is relevant to a particular input question. Given the fact that such matching does not examine a question or an answer individually, context information outside the sentence should be considered equally important to the within-sentence syntactic context. This motivates us to design a new question-answer matching model, built upon a cross-sentence, context-aware, bi-directional long short-term memory architecture. The interactive attention mechanisms are proposed which automatically select salient positional sentence representations, that contribute more significantly towards the relevance between two question and answer. A new quantity called context information jump is proposed to facilitate the formulation of the attention weights, and is computed via the joint states of adjacent words. An interactive-aware sentence representation is constructed by connecting a combination of multiple sentence positional representations to each hidden representation state. In the experiments, the proposed method is compared with existed models, using four public community datasets, and the evaluations show that it is very competitive. In particular, it offers 0.32%-1.8% improvement over the best performing model for three out of four datasets, while for the remaining one performance is around 0.2% of the best performer.
Intensifying Multimedia Information Security Using Comprehensive Cipher

Now a days the world is getting digitalized. The digital technologies are important in everyday life. In this digital world providing security to sensitive data like medical records, bank details are also important. So, Information Security is primary concern for every communication system. The continuous growth of Internet and many other new technologies has made the extensive use of multimedia files unavoidable. The most of the existing studies have concentrated on information security mechanisms only. In this paper, proposed a framework for protecting the multimedia medical records. This system can able to work based on public key Infrastructure cipher as comprehensive cipher. The main strength of this work is analyzed its complexity based on the computational time over the integrated data.
Exploring the Use of Augmented Reality in a Kinesthetic Learning Application Integrated with an Intelligent Virtual Embodied Agent

Technology in education is rapidly changing the way that students learn. This allows for the creation of learning tools that provide better interaction, creative engagement and adaptability to a learner. Augmented Reality (AR) is one of these emerging technologies which can facilitate the development of new learning tools. AR has successfully been proven to allow new types of learning pedagogies by providing human-centered learning environments. In particular, the pedagogical approach of Kinesthetic learning or ”Learning by Doing” has not been explored in great detail in combination with Augmented Reality. This approach is to physically act out an activity to aid in the learning process and has been previously proven as one of the most successful approaches. For a successful application of this pedagogy, the student must get precise feedback and be guided through a process, thus some form of intelligent guide needs to be actively monitoring the learning environment. This paper presents the exploration of this concept through the presentation of an initial prototype system that was developed and implemented based on an adaptive learning methodology within an AR application, with the prospect that in the future will use intelligent agents.
An Incremental Self-Labeling Strategy for Semi-Supervised Deep Learning Based on Generative Adversarial Networks

The recent success of deep neural networks is attributed in part to large-scale well-labeled training data. However, with the ever-increasing size of modern datasets, combined with the difficulty of obtaining label information, semi-supervised learning (SSL) has become one of the most remarkable issues in data analysis. In this paper, we propose an Incremental Self-Labeling strategy for SSL based on Generative Adversarial Nets (ISL-GAN), which functions by constantly assigning unlabeled data with virtual labels for promoting the training process. Specifically, during the virtual labeling process, we introduce a temporal-based self-labeling strategy for safe and stable data labeling. Then, to dynamically assign more virtual labels to data during the training, we conduct a phased incremental label screening and updating strategy. Finally, to balance the contribution of samples with different loss during the training process, we further introduce the Balance factor Term (BT). The experimental results show that the proposed method gives rise to state-of-the-art semi-supervised learning results for the MNIST, CIFAR-10, and SVHN datasets. Particularly, our model performs well with fewer labeled conditions. With a dataset of only 1,000 labeled CIFAR-10 images with CONV-Large Net, a test error of 11.2% can be achieved, and nearly the same performance with a 3.5% test error can be achieved with both 500 and 1,000 image-labeled SVHN datasets.