Miracle-dz/UniFAR-FTUs huggingface dataset:
Odd-Even Embedding Scheme Based Modified Reversible Watermarking Technique using Blueprint

Digital watermarking is a technique of information adding or information hiding in order to identify the owner of the data in multimedia content. It seems that a signal or digital image can permanently embed over another digital data providing a good way to protect intellectual property from illegal replication. The cover data that is transmitted through the internet hides the watermark in a computer aided assertion method such that it becomes undetectable. Finally it stands as a hindrance over many operations without harming the embedded host document. Unfortunately, many owners of the digital materials such as images, text, audio and video are reluctant to the spreading of their documents on the web or other networked environment, because the ease of duplicating digital materials facilitates copyright violation. Digital media distribution occurs through various channels. The cover data may or may not hold any relation with the watermark information. In the last two decades, a considerable amount of research has been done on the digital watermarking of multimedia files such as audio, video, images and text. Different type of watermarking algorithms has been proposed by the researchers to achieve high level of security and authenticity. In our proposed method, a modified reversible watermarking technique is introduced, which employs a blueprint generation of original image based on odd-even embedding methodology to yield large data hiding capacity, security as well as high watermarked quality. The experimental results demonstrate that, no matter how much secret data is embedded, the watermarked quality is about 51dB in this proposed scheme.
Considering the User in Mixed-Initiative Meeting Management

This paper describes a multi-agent system able to manage the meeting schedule of a set of users. The problem of meeting scheduling has been considered because represents an example of how routine daily activities may be delegated to software agent to relief activity overloading of human agents. In the paper a general agent architecture is described which is used to realize di erent kinds of agent in a speci c system called MASMA (Multi Agent System for Meeting Automation). The way MASMA addresses various aspects of the agenda management problem is described and, in particular, several issues concerning the acceptability of the agent approach by human users are discussed. To increase acceptability, an important aspect is the attention paid Partially supported by Modelage and SARI. Work carried out in the framework of the agreement between the FUB and the Italian PT Admnistration.
Multiple sound source tracking and identification via degenerate unmixing estimation technique and cardinality balanced multi-target multi-bernoulli filter (DUET-CBMeMBer) with track management

In Source Separation research, "cocktail party problem" is a challenging problem that research into source separation aims to solve. Many attempts have been made to solve this complex problem. A logical approach would be to break down this complex problem into several smaller problems which are solved in different stages - each considering various aspects. In this paper, we are providing a robust solution to a part of the problem by localizing and tracking multiple moving speech sources in a room environment. Here we study the separation problem for unknown number of moving sources. The DUET-CBMeMBer method we outline is capable of estimating the number of sound sources as well as tracking and labelling them. This paper proposes a track management technique that identifies sound sources based on their trajectory as an extension to the DUET-CBMeMBer technique.
Initiation of an Inflammatory Response in Resident Intestinal Lamina Propria Cells -Use of a Human Organ Culture Model

Resident human lamina propria immune cells serve as powerful effectors in host defense. Molecular events associated with the initiation of an intestinal inflammatory response in these cells are largely unknown. Here, we aimed to characterize phenotypic and functional changes induced in these cells at the onset of intestinal inflammation using a human intestinal organ culture model. In this model, healthy human colonic mucosa was depleted of epithelial cells by EDTA treatment. Following loss of the epithelial layer, expression of the inflammatory mediators IL1B, IL6, IL8, IL23A, TNFA, CXCL2, and the surface receptors CD14, TLR2, CD86, CD54 was rapidly induced in resident lamina propria cells in situ as determined by qRT-PCR and immunohistology. Gene microarray analysis of lamina propria cells obtained by laser-capture microdissection provided an overview of global changes in gene expression occurring during the initiation of an intestinal inflammatory response in these cells. Bioinformatic analysis gave insight into signalling pathways mediating this inflammatory response. Furthermore, comparison with published microarray datasets of inflamed mucosa in vivo (ulcerative colitis) revealed a significant overlap of differentially regulated genes underlining the in vivo relevance of the organ culture model. Furthermore, genes never been previously associated with intestinal inflammation were identified using this model. The organ culture model characterized may be useful to study molecular mechanisms underlying the initiation of an intestinal inflammatory response in normal mucosa as well as potential alterations of this response in inflammatory bowel disease.
Multi-step binary-weighted capacitive digital-to-analog converter architecture

We present a capacitive digital-to-analog converter (DAC) architecture combining properties of the binary-weighted and serial charge-redistribution DACs to yield high integration density and high accuracy. The architecture provides the flexibility to trade area with conversion speed based on a set of area-speed-linearity constraints. We validate the architecture using a 10-bit two-step DAC example, simulated in a standard 0.35 mum CMOS technology. The 10-bit DAC occupies 32 times less area than the conventional 10-bit binary-weighted DAC, has low INL, good matching, and high tolerance to parasitic capacitance.
Reconfigurable hardware OS prototype

In this paper, we approach the rather new area of reconfigurable hardware operating systems in a top-down manner. First, we describe a design concept that defines basic abstractions and operating system services in a deviceindependent way. Then, we refine this model to an implementation concept on the Xilinx Virtex XCV-800 technology. The implementation concept proposes a multitasking environment that executes relocatable hardware tasks, uses a memory management unit translating task requests to internal and external memory accesses, and relies on device drivers and triggers to connect to external I/O. Finally, we present a detailed prototypical implementation and an application case study. The application consists of a set of dynamically loaded and executed networking and multimedia tasks such as IP packet processing, AES decryption, and audio stream decoding.
Euclidean Distance Reconstruction from Partial Distance Information

Euclidean distance matrix completion problem aims at reconstructing the low dimensional geometry structure of nodes given only a few pairwise distances between nodes [1]. This problem arises in many applications including networks and machine learning where much information of points is laking. For instance, in sensor networks, due to the constraints of energy and communication radius, sensor nodes can only measure the pairwise distances accurately between their nearby nodes. As a consequence, we can only form a partial Euclidean distance matrix. Now, an interesting question is: given this partial Euclidean distance matrix, can we reconstruct or estimate the Euclidean distance matrix exactly? Many works are based on heuristic algorithms without rigorous mathematical justification. However, recent landmark work in low-rank matrix reconstruction [2][3][4] provides some new directions on attacking this problem. One of the central message of these works is the feasibility of exact matrix reconstruction by solving a tractable semidefinite programming problem, with a few entries at hand. We notice that the Euclidean distance matrix is extremely low rank considering the fact that nodes are located in low-dimensional space (typically of dimensions two or three). In particular, the rank of the Euclidean distance matrix is at most d + 2, where d is the dimension of the coordinates of node locations. This fact motivates us to apply the low rank matrix completion theory to the Euclidean distance matrix completion problem.
Weaver: A High-Performance, Transactional Graph Store Based on Refinable Timestamps

Distributed systems for storing and processing large graphs have become an increasingly common infrastructure component. Yet existing systems either operate on offline snapshots, provide weak consistency guarantees for dynamic graphs, or use expensive concurrency control techniques that limit performance. In this paper, we introduce a new distributed graph store, called Weaver, which enables efficient, transactional graph analyses as well as strictly serializable read-write transactions on dynamic graphs. The key insight that allows Weaver to combine strict serializability with horizontal scalability and high performance is a novel request ordering mechanism called refinable timestamps. This technique couples coarsegrained vector timestamps with a fine-grained timeline oracle to pay the overhead of strong consistency only when needed. Experiments show that Weaver enables a Bitcoin blockchain explorer that is 8× faster than Blockchain.info, and achieves 12× higher throughput than the Titan graph database on social network workloads and 4× lower latency than GraphLab on offline graph traversal workloads.
Dynamic warp formation: Efficient MIMD control flow on SIMD graphics hardware

Recent advances in graphics processing units (GPUs) have resulted in massively parallel hardware that is easily programmable and widely available in today's desktop and notebook computer systems. GPUs typically use single-instruction, multiple-data (SIMD) pipelines to achieve high performance with minimal overhead for control hardware. Scalar threads running the same computing kernel are grouped together into SIMD batches, sometimes referred to as warps. While SIMD is ideally suited for simple programs, recent GPUs include control flow instructions in the GPU instruction set architecture and programs using these instructions may experience reduced performance due to the way branch execution is supported in hardware. One solution is to add a stack to allow different SIMD processing elements to execute distinct program paths after a branch instruction. The occurrence of diverging branch outcomes for different processing elements significantly degrades performance using this approach. In this article, we propose dynamic warp formation and scheduling, a mechanism for more efficient SIMD branch execution on GPUs. It dynamically regroups threads into new warps on the fly following the occurrence of diverging branch outcomes. We show that a realistic hardware implementation of this mechanism improves performance by 13%, on average, with 256 threads per core, 24% with 512 threads, and 47% with 768 threads for an estimated area increase of 8%.
Now I know what ICT can do for me!

The under‐representation of women entering into information and communication technology (ICT) programmes is a long‐standing problem. While ICT continues to be increasingly accepted and integrated into everyday life, gender stereotypes prevail in ICT turning female students away from ICT as an occupation. Based on existing literature on factors influencing girls' career choices and on an empirical qualitative study in three schools in Australia, a conceptualisation of the influence of middle‐school experiences on girls' ICT study and career choices is presented. This conceptualisation is used as a guide to evaluate a recent intervention programme aimed at promoting ICT study and work opportunities to middle‐school girls. A number of recommendations for future programmes are provided, highlighting the need to study how subcultures that support ICT career choices develop and are supported in Australian schools.
Human Performance on Clustering Web

With the increase in information on the World Wide Web it has become difficult to find desired information quickly without using multiple queries or using a topic-specific search engine. One way to help in the search is by grouping HTML pages together that appear in some way to be related. In order to better understand this task, we performed an initial study of human clustering of web pages, in the hope that it would provide some insight into the difficulty of automating this task. Our results show that subjects did not cluster identically; in fact, on average, any two subjects had little similarity in their web-page clusters. We also found that subjects generally created rather small clusters, and those with access only to URLs created fewer clusters than those with access to the full text of each web page. Generally the overlap of documents between clusters for any given subject increased when given the full text, as did the percentage of documents clustered. When analyzing individual subjects, we found that each had different behavior across queries, both in terms of overlap, size of clusters, and number of clusters. These results provide a sobering note on any quest for a single clearly correct clustering method for web pages.1
Large Particulate Antigens Initiating T-Dependent B Cell Responses to A Protease-Dependent Mechanism for Rooijen and

Ab production is critical for antimicrobial immunity, and the initial step in this process is the binding of Ag to the BCR. It has been shown that small soluble proteins can directly access the lymph node follicles to reach naive B cells, but virus particles must be trans-located into follicles via subcapsular sinus macrophages. In this article, we explore how large particulate Ags generate humoral immune responses. Ag-specific follicular B cells rapidly acquired Ag, presented peptide:MHC class II ligands, and produced T-dependent Ab responses following s.c. injection of 1-mm, Ag-linked microspheres, despite the microspheres being confined to the subcapsular sinus. The mechanism of Ag acquisition did not require dendritic cells, subcapsular sinus macrophages, or B cell movement to the subcapsular sinus. Rather, B cell Ag acquisition was protease-dependent, suggesting that some protein Ags are cleaved from the surface of particles to directly initiate humoral immune responses. I nitiation of the humoral immune response requires binding of Ag to the BCR. Recognition of Ag by the BCR results in a cascade of protein tyrosine phosphorylation events that promotes the expression of costimulatory and homing molecules, such as CD86, CD40, and CCR7 (1, 2). Activated B cells migrate to the border between the follicle and the T cell area where they display Ag-derived peptides, in the context of MHC class II (MHC II) molecules, to activated CD4 + T cells (3). After receiving signals from T cells, B cells divide and switch isotypes (4). Some of the progeny establish residence in the paracortex and become Ab-secreting plasmablasts, whereas others migrate back to the follicle and participate in ger-minal center reactions, where they undergo affinity maturation (5). Ags must be carried from tissues to secondary lymphoid organs where naive lymphocytes reside before the aforementioned processes can occur. Following infection of tissue, microbes and their secreted proteins are carried through afferent lymphatic vessels and then deposited into the subcapsular and paracortical sinuses of the draining lymph node (6, 7). From here, soluble proteins or small viruses can enter the conduit network (8), which serves as a series of tubes that connect the subcapsular sinus to the perivenular space, as well as a scaffolding on which T cells migrate through the lymph node (9). The conduit network consists of a collagen core, wrapped within a microfibrillar matrix and basement membrane, and surrounded by fibroblastic reticular cells (10). Lymph node-resident dendritic cells (DCs) are found between …
A Hadoop-based visualization and diagnosis framework for earth science data

With rapidly growing computing power, ultra high-resolution Earth science simulations with a long period of time are feasible. However, it is still very challenging to distribute and analyze a huge amount of simulation results, which could be over 100TB. One key reason is that typical Earth science data are represented in NetCDF, which is not supported by the popular and powerful Hadoop Distribute File System (HDFS) and consequently cannot be analyzed with tools based on HDFS. In this paper, we propose a Hadoop-based visualization and diagnosis framework for visualizing and analyzing Earth science data. It has a data model to transform data from the format of NetCDF to CSV (Comma Separated Value) that is supported by HDFS. With this model, data can be processed with the operations such as maximize, sum, and subset through HIVE and Cloudera Impala and, therefore, typical diagnoses can be performed. In addition, the framework has a technique to visualize and diagnose HDFS-resident data with the popular visualization and diagnosis tool, IDL. To speed up this process, a concurrent reader is developed to obtain HDFS-resident data. Moreover, a dynamic reader to transfer data from a parallel file system (PFS) to HDFS is developed to efficiently visualize and diagnose PFS-resident data. The cloud resolve mode simulations are used for testing and evaluating this framework.
Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection

Outlier detection in high-dimensional data presents various challenges resulting from the “curse of dimensionality. ” A prevailing view is that distance concentration, i.e., the tendency of distances in high-dimensional data to become indiscernible, hinders the detection of outliers by making distance-based methods label all points as almost equally good outliers. In this paper, we provide evidence supporting the opinion that such a view is too simple, by demonstrating that distance-based methods can produce more contrasting outlier scores in high-dimensional settings. Furthermore, we show that high dimensionality can have a different impact, by reexamining the notion of reverse nearest neighbors in the unsupervised outlier-detection context. Namely, it was recently observed that the distribution of points' reverse-neighbor counts becomes skewed in high dimensions, resulting in the phenomenon known as hubness. We provide insight into how some points (antihubs) appear very infrequently in k-NN lists of other points, and explain the connection between antihubs, outliers, and existing unsupervised outlier-detection methods. By evaluating the classic k-NN method, the angle-based technique designed for high-dimensional data, the density-based local outlier factor and influenced outlierness methods, and antihub-based methods on various synthetic and real-world data sets, we offer novel insight into the usefulness of reverse neighbor counts in unsupervised outlier detection.
Markov chain decomposition for convergence rate analysis

In this paper we develop tools for analyzing the rate at which a reversible Markov chain converges to stationarity. Our techniques are useful when the Markov chain can be decomposed into pieces which are themselves easier to analyze. The main theorems relate the spectral gap of the original Markov chains to the spectral gap of the pieces. In the first case the pieces are restrictions of the Markov chain to subsets of the state space; the second case treats a Metropolis-Hastings chain whose equilibrium distribution is a weighted average of equilibrium distributions of other Metropolis-Hastings chains on the same state space.
The Affect of Multi-source Time Delay to the Human Balance Based on R obust Control Model

Time-delay is a prevalent phenomenon in human body during standing and locomotion, and it is one of the main factors to affect human balance ability. The purpose of this study is to design a human balance control model, and to analyze the human possible posture control mechanism with multi-source delay considered. Based on an inverted pendulum with the ankle joint model, we derived and proved that the human body existed multi-source time delay, and then designed a robust state feedback controller with an observer for the generalized system, which is with delay state and delay control inputs in continuous time. This controller can be considered as the central nervous system (CNS) for the human balance controller mathematical model. Finally, applying simulation software, we obtained the balance regulation kinematics responses to anterior and posterior (A/P) surface translations, and discussed the effect on the dynamic response of the human body to different kinds of the time-delay. The robust controller can stable the human system within larger interference and larger time-delay. This paper provides a useful method for analyzing the impact of the multisource time delay on the human balance system.
Some Generalized Neutrosophic Number Hamacher Aggregation Operators and Their Application to Group Decision Making

The neutrosophic set (NS) can be better to express the incomplete, indeterminate and inconsistent information, and Hamacher aggregation operators extended the Algebraic and Einstein aggregation operators and the generalized aggregation operators can generalize the arithmetic, geometric, and quadratic aggregation operators. In this paper, we combined Hamacher operations and generalized aggregation operators to NS, and proposed some new aggregation operators. Firstly, we presented some new operational laws for neutrosophic numbers (NNs) based on Hamacher operations and studied their properties. Then, we proposed the generalized neutrosophic number Hamacher weighted averaging (GNNHWA) operator, generalized neutrosophic number Hamacher ordered weighted averaging (GNNHOWA) operator, and generalized neutrosophic number Hamacher hybrid averaging (GNNHHA) operator, and explored some properties of these operators and analyzed some special cases of them. Furthermore, we gave a new method based on these operators for multiple attribute group decision making problems with neutrosophic numbers, and the operational steps were illustrated in detail. Finally, an application example is given to verify the proposed method and to demonstrate its effectiveness.
Time to Decide: Information Search and Revelation in Groups PRELIMINARY AND INCOMPLETE

We analyze costly information acquisition and information revelation in groups evaluating dif- ferent decision options in a dynamic setting. Even when group members have perfectly aligned interests the group may ine¢ ciently delay decisions. When deadlines are absent or far, unin- formed group members freeride on each others'eorts to acquire information. When deadlines come close, successful group members stop revealing their information in an attempt to incen- tivize others to continue searching for information. Surprisingly, setting a tighter deadline may increase the expected decision time and increase the expected accuracy of the decision in the unique equilibrium. As long as the deadline is set optimally, welfare is higher when information is only privately observable to the agent who obtained information rather than to the entire group.