Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of Buddle Protocol of Deep Space Sensor Network Based on DTN

This deep space sensor network based on DTN is an important research in modern deep space communications technologies. Bundle layer plays a key role in the network. How Bundle layer can provide services for network more rationally and efficiently is an important influencing factor to improve and enhance the network performance. This paper proposes special network architecture for this DTN-based network. Systematic study on function and structure of Bundle Protocol which runs in corresponding layer is completed. To realize this protocol, a scheme based on different functional modules is proposed. In order to solve the congestion problems in the deep-space wireless sensor networks, referencing the previous studies, this paper proposes a new congestion control algorithm which bases on persisting storing and transferring the data. Through simulation experiments, comparing to the existing congestion control algorithm, the new algorithm effectively reduces the data loss rate and the average latency in the network.
Benchmarking Soundtrack Recommendation Systems with SRBench

In this work, a benchmark to evaluate the retrieval performance of soundtrack recommendation systems is proposed. Such systems aim at finding songs that are played as background music for a given set of images. The proposed benchmark is based on preference judgments, where relevance is considered a continuous ordinal variable and judgments are collected for pairs of songs with respect to a query (i.e., set of images). To capture a wide variety of songs and images, we use a large space of possible music genres, different emotions expressed through music, and various query-image themes. The benchmark consists of two types of relevance assessments: (i) judgments obtained from a user study, that serve as a "gold standard" for (ii) relevance judgments gathered through Amazon's Mechanical Turk. We report on an analysis of relevance judgments based on different levels of user agreement and investigate the performance of two state-of-the-art soundtrack recommendation systems using the proposed benchmark.
Sub-Profiling by Linguistic Dimensions to Solve the Authorship Attribution Task

In this paper, we describe a modified version of the profile-based ap- proach for the Authorship Attribution (AA) task of the PAN 2012 challenge. Our PAN system for AA utilizes the concept of linguistic modalities 1 on profile-based (PB) approaches. We concatenate all the training documents from the same author and build author-specific sub-profiles, one per linguistic modality. Then instead of using all the different types of features to compute the similarity of a test doc- ument against an author's profile in a single step, we compute several similarity scores using one set of features (modality) at a time. Each modality will assign the test document to the author whose sub-profile has the highest cosine similarity in that modality. Final classification decisions are based on the combination of deci- sions from each modality using majority voting. We achieved competitive results on PAN 2012, with encouraging results on the closed-class authorship attribution.
Higher-order modules in System F ω and

To argue that it is practical to extend core Haskell to support higher-order modules, we translate Dreyer, Crary, and Harper’s higher-order module system (2002, 2003) into System Fω. Our translation is the first to fully treat generative functors (with existential types) alongside applicative ones (with Skolemized types). Applicative functors correspond to higher-order polymorphism in idiomatic Haskell—abstract, higher-kinded type constructors, accompanied by term combinators. Higher-order functors correspond to higher-rank polymorphism in Fω, which is practical to add to Haskell and has been added (Peyton Jones and Shields 2004). The difference between generative and applicative functors boils down to whether existential type variables scope under or over the typing context. Thus we can express functor bodies that mix generative and applicative parts, which are inconvenient to simulate in Dreyer et al.’s language. Also, we elude the avoidance problem (the lack of minimal supersignatures) because existential quantification is primitive in Fω. Although modules are first-class values, type sharing and the phase distinction are both preserved: the programmer can make fine-grained trade-offs between opaque and transparent types, and two modules are statically equivalent just in case they have the same type. Our work can guide further improvements to modularity in Haskell and ML.
Dynamic location update for PCS networks with consideration of MT mobility patterns

This paper addresses the dynamic location update for personal communication service (PCS) networks. The four-direction mobility model for the popular grid cellular architectures is introduced to exploit the mobility patterns of all kinds of mobile terminals (MTs). The shape and size of the local area (LA) in the dynamic location update scheme is determined by minimizing the total location management cost with bounding the paging cost. The optimization problem is transferred to maximize expected number of cells traversed by the MT in the LA with a given size of the LA. The analytic model of calculating the probabilities of any MT's moving is established. An algorithm is proposed to solve the optimization problem efficiently.
USING DATA STRIPING IN A LOCAL AREA NETWORK

We use the technique of storing the data of a single object across several storage servers, called data striping, to achieve high transfer data rates in a local area network. Using parallel paths to data allows a client to transfer data to and from storage at a higher rate than that supported by a single storage server. We have imple- mented a network data service, called Swift, that uses data striping. Swift exhibits the expected scaling property in the number of storage servers connected to a network and in the number of interconnection networks present in the system. We have also simulated a version of Swift to explore the limits of possible future configurations. We observe that the system can evolve to support very high speed interconnection networks as well as large numbers of storage servers. Since Swift is a distributed system made up of independently replaceable components, any component that limits the performance can either be *replaced* by a faster component when it becomes available or can be *replicated* and used in parallel. This should allow the system to incorporate and exploit emerging storage and networking technologies.
The Corpus Callosum and the Visual Cortex: Plasticity Is a Game for Two

Throughout life, experience shapes and selects the most appropriate brain functional connectivity to adapt to a changing environment. An ideal system to study experience-dependent plasticity is the visual cortex, because visual experience can be easily manipulated. In this paper, we focus on the role of interhemispheric, transcallosal projections in experience-dependent plasticity of the visual cortex. We review data showing that deprivation of sensory experience can modify the morphology of callosal fibres, thus altering the communication between the two hemispheres. More importantly, manipulation of callosal input activity during an early critical period alters developmental maturation of functional properties in visual cortex and modifies its ability to remodel in response to experience. We also discuss recent data in rat visual cortex, demonstrating that the corpus callosum plays a role in binocularity of cortical neurons and is involved in the plastic shift of eye preference that follows a period of monocular eyelid suture (monocular deprivation) in early age. Thus, experience can modify the fine connectivity of the corpus callosum, and callosal connections represent a major pathway through which experience can mediate functional maturation and plastic rearrangements in the visual cortex.
The Gest-Rest Family

Interacting with touch screen-based computing devices can be difficult for individuals with mobility impairments that affect their hands, arms, neck, or head. These problems may be especially difficult for power wheelchair users, as the frame of their wheelchair may obstruct the users’ range of motion and reduce their ability to reach objects in the environment. The concept of “chairable” input devices refers to input devices that are designed to fit with the form of an individual's wheelchair, much like wearable technology fits with an individual's clothing. In this paper, we introduce a new family of chairable input devices: Gest-Rests. A Gest-Rest is a gesture based input device that fits over a standard power wheelchair armrest. A Gest-Rest enables users to perform traditional touch screen gestures, such as press and flick, as well as pressure-based gestures such as squeezing and punching. We have developed the Gest-Rest Family, a suite of armrest-based input devices designed for power wheelchair users. In this paper, we present formative evaluations of these prototypes with wheelchair users and therapists and present their opinions of this new integrated input technology. Our results show the benefits of armrest-based input devices as well as a comparison of physical switches, force sensing, and capacitive input for individuals with limited dexterity.
Stabilization of LKB1 and Akt by neddylation regulates energy metabolism in liver cancer

The current view of cancer progression highlights that cancer cells must undergo through a post-translational regulation and metabolic reprogramming to progress in an unfriendly environment. In here, the importance of neddylation modification in liver cancer was investigated. We found that hepatic neddylation was specifically enriched in liver cancer patients with bad prognosis. In addition, the treatment with the neddylation inhibitor MLN4924 in Phb1-KO mice, an animal model of hepatocellular carcinoma showing elevated neddylation, reverted the malignant phenotype. Tumor cell death in vivo translating into liver tumor regression was associated with augmented phosphatidylcholine synthesis by the PEMT pathway, known as a liver-specific tumor suppressor, and restored mitochondrial function and TCA cycle flux. Otherwise, in protumoral hepatocytes, neddylation inhibition resulted in metabolic reprogramming rendering a decrease in oxidative phosphorylation and concomitant tumor cell apoptosis. Moreover, Akt and LKB1, hallmarks of proliferative metabolism, were altered in liver cancer being new targets of neddylation. Importantly, we show that neddylation-induced metabolic reprogramming and apoptosis were dependent on LKB1 and Akt stabilization. Overall, our results implicate neddylation/signaling/metabolism, partly mediated by LKB1 and Akt, in the development of liver cancer, paving the way for novel therapeutic approaches targeting neddylation in hepatocellular carcinoma.
Deep Collaborative Learning for Visual Recognition

Deep neural networks are playing an important role in state-of-the-art visual recognition. To represent high-level visual concepts, modern networks are equipped with large convolutional layers, which use a large number of filters and contribute significantly to model complexity. For example, more than half of the weights of AlexNet are stored in the first fully-connected layer (4,096 filters). We formulate the function of a convolutional layer as learning a large visual vocabulary, and propose an alternative way, namely Deep Collaborative Learning (DCL), to reduce the computational complexity. We replace a convolutional layer with a two-stage DCL module, in which we first construct a couple of smaller convolutional layers individually, and then fuse them at each spatial position to consider feature co-occurrence. In mathematics, DCL can be explained as an efficient way of learning compositional visual concepts, in which the vocabulary size increases exponentially while the model complexity only increases linearly. We evaluate DCL on a wide range of visual recognition tasks, including a series of multi-digit number classification datasets, and some generic image classification datasets such as SVHN, CIFAR and ILSVRC2012. We apply DCL to several state-of-the-art network structures, improving the recognition accuracy meanwhile reducing the number of parameters (16.82% fewer in AlexNet).
Power flow transferring path search and emergency control strategy

In order to eliminate the cascading overload caused by power flow transferring, the paper puts forward a method for fast search of dangerous lines in power flow transferring and an emergency control strategy considering the balancing machine. When the fault line is disconnected, search the shortest path between the end nodes of the broken line. Monitor the lines on the path emphatically. If the lines on the path are overload, lock their backup protection. According to the GSDF of each node to the overload lines, considering the balancing machine participating in emergency control, determine the control node and adjustment power to eliminate the overload rapidly. The method is verified in the IEEE39 bus system.
On using Articulatory Features in Posterior-based ASR System: Representation, Estimation and Integration

Articulatory features describe the properties of speech production, i.e., each sound unit of a language (phone) can be decomposed into a set of features based on the articulators used to produce the sound. Articulatory features (AFs) have been used for automatic speech recognition (ASR) with the aim of better pronunciation modeling [1], robustness to noise [2], multi-lingual and cross-lingual portability of the systems [3] etc. In case of text-to-speech (TTS) conversion systems AFs are explored to achieve emotional speech synthesis. ASR or TTS us-ing AFs poses three main challenges:
Multi-threaded collision-aware global routing with bounded-length maze routing

Modern global routers use various routing methods to improve routing speed and the quality. Maze routing is the most time-consuming process for existing global routing algorithms. This paper presents two bounded-length maze routing (BLMR) algorithms (optimal-BLMR and heuristic-BLMR) to perform much faster routing than traditional maze routing algorithms. The proposed sequential global router, which adopts a heuristic-BLMR, identifies less-wirelength routing results with less runtime than state-of-the-art global routers. This study also proposes a parallel multi-threaded collision-aware global router based on a previous sequential global router. Unlike the conventional partition-based concurrency strategy, the proposed algorithm uses a task-based concurrency strategy. Experimental results reveal that the proposed sequential global router uses less wirelength and runs about 1.9X to 18.67X faster than other state-of-the-art global routers. Compared to the proposed sequential global router, the proposed parallel global router yields almost the same routing quality with average 2.71 and 3.12-fold speedup on overflow-free and hard-to-route benchmarks, respectively, when running on an Intel quad-core system.
Verbal and spatial working memory in school-age children: developmental differences in susceptibility to interference.

The development of verbal and spatial working memory was investigated with an interference paradigm. Memory spans were obtained from 3 groups (8-, 10-, and 19-year-olds) under 6 different conditions: Two primary memory tasks (1 verbal, 1 spatial) were administered in isolation and in conjunction with 2 versions of a secondary task. The primary tasks required recalling a series of visually presented digits and recalling the locations of Xs in a series of visually presented grids. The secondary tasks required reporting the color of the stimuli as they were presented using either a verbal or a spatial response. Analyses revealed that all age groups showed domain-specific interference (i.e., interference by a secondary task from the same domain as the primary task), but only the 8-year-olds also showed nonspecific interference (i.e., interference by a secondary task from a domain different than the primary memory task), suggesting that at least some executive functions do not reach adult levels of efficiency until approximately age 10.
Basic Properties of the Rank of Matrices over a Field

Basic Properties of the Rank of Matrices over a Field In this paper I present selected properties of triangular matrices and basic properties of the rank of matrices over a field. I define a submatrix as a matrix formed by selecting certain rows and columns from a bigger matrix. That is in my considerations, as an array, it is cut down to those entries constrained by row and column. Then I introduce the concept of the rank of a m x n matrix A by the condition: A has the rank r if and only if, there is a r x r submatrix of A with a non-zero determinant, and for every k x k submatrix of A with a non-zero determinant we have k ≤ r. At the end, I prove that the rank defined by the size of the biggest submatrix with a non-zero determinant of a matrix A, is the same as the maximal number of linearly independent rows of A.
PREDICTION OF MACHINING OPERATIONS AND SURFACE ROUGHNESS USING ARTIFICIAL NEURAL NETWORK

Surface roughness is considered as one of the most specified customer requirements in machining processes. For efficient use of machine tools, selection of machining process and determination of optimal cutting parameters (speed, feed and depth of cut) are required. Therefore, it is necessary to find a suitable way to select and to find optimal machining process and cutting parameters for a specified surface roughness values. In this work, machining process was carried out on AISI 1040 steel in dry cutting condition in a lathe, milling and grinding machines and surface roughness was measured. Forty five experiments have been conducted using varying speed, feed, and depth of cut in order to find the surface roughness parameters. This data has been divided into two sets on a random basis; 36 training data set and 9 testing data set. The training data set has been used to train different artificial neural network (ANN) models in order to predict machining processes and surface roughness parameter values through back propagation network. Experimental data collected from tests were used as input parameters of a neural network to identify the sensitivity among machining operations, cutting parameters and surface roughness. Selected indexes were used to design a suitable algorithm for the prediction of machining processes. A software was developed and implemented to predict the machining processes and surface roughness values. The results showed that the proposed models are capable of predicting machining operations, cutting parameters and surface roughness.
Emergence of temporal and spatial synchronous behaviors in a foraging swarm

Biological populations often exhibit complex and efﬁcient behaviors, where temporal and spatial couplings at the macro-scale population level emerge from interactions at the micro-scale individual level, without any centralized control. This paper speciﬁcally investigates the emergence of behavioral synchronization and the division of labor in a foraging swarm of robotic agents. A deterministic model is proposed and used by each agent to decide whether it goes foraging, based on local cues about its fellow ants' behavior. This individual model, based on the competition of two spiking neurons, results in a self-organized division of labor at the population level. Depending on the strength and occurrences of interactions among individuals, the population behavior displays either an asynchronous, or a synchronous aperiodic, or a synchronous periodic division of labor. Further, the beneﬁts of synchronized individual behaviors in terms of overall foraging efﬁciency are highlighted in a 2D spatial simulation.
An Annotation Process for Data Visualization Techniques

As the area of information visualization grows, a massive amount of visualization techniques has been developed. Consequently, the choice of an appropriate visualization has become more complex, usually resulting in unsatisfactory data analysis. Although there exist models and classifications that could guide the choice of a visualization technique, they are mostly generalist and do not present a clear methodology for evaluation and evolution. In contrast, we propose an annotation process for data visualization techniques based on an initial capability-driven collection of terms and concepts that encompasses visual components of both well established as well as modern visualization techniques. To demonstrate the initial collections expressiveness, we present a qualitative analysis of an experiment with specialist users at annotating visualization techniques from the D (Data-Driven Documents) library. Furthermore, to show the completeness of the collection, we automatically assess its coverage of all published papers from six major international information visualization conferences since 1995. Our results attest the expressiveness of the initial collection and its coverage of over 99% of the analysed literature. Finally, we discuss the limitations and alternatives for semi-automatically evolving the annotation process as new visualization techniques are developed and how the spread of this type of methodology could benefit the information visualization community. Keywords–Annotation Process; Data Visualization; Ontologies; Taxonomies.
Intelligent multi-level regions-of-interest (ROI) document image encryption using an online learning model

Image-based document management systems have become increasingly popular for handling documents that contain both text and graphical elements. When such systems are used to store confidential information, document security is a key concern. Conventional encryption techniques used for images fail to provide the level of flexibility required by such document management systems. Newer image encryption techniques provide improved flexibility at the cost of backwards compatibility and ease of use. This paper presents a novel approach to document image encryption using an online learning model. The proposed system provides backwards-compatible document image encryption in regions of interest (ROI) with support for multiple levels of authority. Furthermore, the proposed system is capable of learning from user feedback to improve the ROI selection used during the semi-automatic document encryption process. Experimental results from the encryption of test documents demonstrate the effectiveness of the proposed system.
Traffic engineering for MPLS-based virtual private networks

This paper considers the traffic engineering of MPLS-based virtual private networks (VPNs) with multiple classes of service. We focus on two main issues. Firstly, we point out that the one LSP per ingress-egress pair constraint can be relaxed for the case of MPLS-based VPNs due to the ease in classifying flows on a per-VPN basis. This allows us to use LSP with finer granularity and thus better load balancing. Secondly, we point out that the single objective traffic engineering formulations proposed in literature address only one particular aspect of the traffic engineering problem. We propose a multiobjective traffic engineering problem which takes both resource usage and link utilisation into account. This optimisation problem is NP-complete and involves a large number of variables. We propose an heuristic to solve this problem.