Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed feature expansion-based short text classification

To cope with the characteristics taken on short text classification, feature sparse and lack of background features in the field, a mixed feature expansion-based text Categorization is proposed, incorporating the Latent Dirichlet Allocation (LDA) and Frequent Pattern growth (FP-Growth). First and foremost, the theme words were extracted by LDA topic model as features and thereupon added to the original feature to first phase feature expansion. Secondly, given that the frequent probability index varies with the classes of word sets, different categories of basic confidence are set for the text. Furthermore, the high-quality and scalable features are filtered out for second phase feature expansion whereby the FP-Growth algorithm and chi-square test. Experimental results prove the mixed feature expansion method is able to effectively increase the accuracy of short text categorization in news text categorization.
Stochastic modeling, virtual refinement, and deterministic fabrication of tissue engineering scaffolds

Tissue engineering attempts to address the ever widening gap between the demand and supply of organ and tissue transplants using natural and biomimetic scaffolds. The current scaffold fabrication techniques can be broadly classified into (a) conventional, irreproducible, stochastic techniques producing biomorphic "secundam naturam" but sub optimal scaffold architecture and (b) rapidly emerging, repeatable, computer-controlled Solid Freeform Fabrication (SFF) producing, "contra naturam" scaffold architecture. This paper presents an image-based scaffold optimization strategy based on microCT images of the conventional scaffolds. This approach, attempted and perfected for the first time, synergistically exploits the orthogonal techniques to create repeatable, biomorphic scaffolds with optimal scaffold geometry. The ramifications of this image based computer assisted intervention to improve the status quo of scaffold fabrication might contribute to the previously elusive deployment of promising benchside tissue analogs to the clinical bedside.
Load balancing greedy algorithm for reduce on Hadoop platform

MapReduce is a widely used parallel computing framework and is an important part of the Hadoop platform. It mainly includes Map and Reduce functions, and Map function outputs key-value key-value pairs as input for Reduce. Due to the dynamic input, Reduce How to solve the load balancing of Reduce is an important research direction to optimize MapReduce. Taking the whole data as a sample and analyzing the data by the appropriate amount of samples to get a reliable key distribution with a small cost, Algorithm instead of the default Hash algorithm of Hadoop platform to divide the data into Reduce load balancing. The main idea of the proposed greedy algorithm is to calculate the average of all key frequencies and the number of Reduce nodes according to the sampling data and then allocate each Reduce A load that is close to the average, so as to achieve the overall load balancing. The simulation results show that compared with the default hash partitioning algorithm, the proposed algorithm can save 10.6% uptime and achieve better load balancing.
Neural Mechanisms Underlying Individual Differences in Control-Averse Behavior

When another person tries to control one's decisions, some people might comply, but many will feel the urge to act against that control. This control aversion can lead to suboptimal decisions and it affects social interactions in many societal domains. To date, however, it has been unclear what drives individual differences in control-averse behavior. Here, we address this issue by measuring brain activity with fMRI while healthy female and male human participants made choices that were either free or controlled by another person, with real consequences to both interaction partners. In addition, we assessed the participants' affects, social cognitions, and motivations via self-reports. Our results indicate that the social cognitions perceived distrust and lack of understanding for the other person play a key role in explaining control aversion at the behavioral level. At the neural level, we find that control-averse behavior can be explained by functional connectivity between the inferior parietal lobule and the dorsolateral prefrontal cortex, brain regions commonly associated with attention reorientation and cognitive control. Further analyses reveal that the individual strength of functional connectivity complements and partially mediates the self-reported social cognitions in explaining individual differences in control-averse behavior. These findings therefore provide valuable contributions to a more comprehensive model of control aversion. SIGNIFICANCE STATEMENT Control aversion is a prevalent phenomenon in our society. When someone tries to control their decisions, many people tend to act against the control. This can lead to suboptimal decisions such as noncompliance to medical treatments or disobeying the law. The degree to which individuals engage in control-averse behavior, however, varies significantly. Understanding the proximal mechanisms that underlie individual differences in control-averse behavior has potential policy implications, for example, when designing policies aimed at increasing compliance with vaccination recommendations, and is therefore a highly relevant research goal. Here, we identify a neural mechanism between parietal and prefrontal brain regions that can explain individual differences in control-averse behavior. This mechanism provides novel insights into control aversion beyond what is accessible through self-reports.
Improving Energy Efficiency of Low-Voltage Logic by Technology-Driven Design

Reducing <inline-formula> <tex-math notation="LaTeX">$V_{\mathbf {DD}}$ </tex-math></inline-formula> while keeping leakage current low is critical for minimizing energy consumption for systems across the compute-continuum, especially in IoT. Emerging low-<inline-formula> <tex-math notation="LaTeX">$V_{\mathbf {DD}}$ </tex-math></inline-formula> logic devices such as tunnel FET (TFETs) offer better low-<inline-formula> <tex-math notation="LaTeX">$V_{\mathbf {DD}}$ </tex-math></inline-formula> performance than conventional MOSFETs but lack performance at high-<inline-formula> <tex-math notation="LaTeX">$V_{\mathbf {DD}}$ </tex-math></inline-formula>. To assess TFETs, and other transistors optimized for low-<inline-formula> <tex-math notation="LaTeX">$V_{\mathbf {DD}}$ </tex-math></inline-formula> operation, we propose a technology-driven design framework. Our framework adapts standard industry flows and tools to optimize the design of logic blocks with full consideration of the tradeoffs possible with the future generation device <inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> characteristics and interconnect. Proposed approach optimizes design implementation to improve projected power-performance and area, as well as, expected accuracy of the projections. TFET improves energy efficiency by 2.35<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> and 1.35<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> over MOSFET at low- and high-performance points, respectively, for industrial design test cases. Accuracy of the energy efficiency and performance projections is improved by 71% and 40%, respectively.
Corynebacterium macginleyi isolation from conjunctival swab in Italy

Corynebacterium macginleyi was isolated from conjunctival swabs of a farmer suffering from purulent conjunctivitis. This species has only recently been reported in Switzerland and Germany to be exclusively isolated from ocular surfaces. This represents the first isolation of C. macginleyiin Italy indicating that its circulation is not geographically limited. © 2002 Elsevier Science Inc. All rights reserved.
Textural Feature Extraction and Analysis for Brain Tumors using MRI

The proposed research work is to perform textural analysis of the brain tumor on MRI images and this process aims by giving correct decisions towards medication and providing tools for automated extraction of the most discerning features of regions of interest in human brain. The detection of tumor in human brain (MRI) is performed through segmentation and for region characterization we use texture information. Based on extracted information the textural analysis of tumor is done. For the selection of region of interest we use FCM and Level set segmentation. After selecting region of interest we extract GLCM features from the segmented brain tumor image. The textural information is captured from the region of interest using Haralick textural descriptor features. To train and classify the tumor into benign or malignant we use SVM classifier. The ultimate aim is to develop an automated classification of region into one of the classes as benign or malignant. The digital medical imaging and clinical data associating on daily data are witnessed by the hospitals all over the world. The expertise will be able to analyze, inspect quickly the images.
What we need to learn if we want to do and not just talk

In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates. Our paper makes the first attempt at evaluating state of the art models on a large real world task with human users. We show that methods that achieve state of the art performance on synthetic datasets, perform poorly in real world dialog tasks. We propose a hybrid model, where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. The hybrid model on the customer support data achieves a 78% relative improvement in fluency, and a 200% improvement in accuracy of external calls.
A TUTORIAL EXAMPLE OF STIMULUS SAMPLE DISCRIMINATION IN PERCEPTUAL EVALUATION OF SYNTHESIZED SOUNDS: DISCRIMINATION BETWEEN ORIGINAL AND RE-SYNTHESIZED SINGING

Stimulus sample discrimination (SSD) is an objective psychophysical procedure, in which samples are drawn from various signal distributions for comparison and an index of discrimination is measured. A key feature of SSD is the use of samples from a context distribution, which act either as additional or as distracting sources of information with respect to the discrimination task. When the context distribution provides information about the natural variations in the sounds from a musical instrument, SSD may prove useful as a measure of the perceptual accuracy of a sound synthesis algorithm. We report on results from a study in which SSD is applied to measure the degree to which singer identity is preserved in loworder synthesis of the female singers.
Oscillating Magnetic Field Mapping using MRI

INTRODUCTION: We present a new method to estimate oscillating magnetic fields in the Kilohertz (KHz) frequency range using magnetic resonance imaging. Our target applications are to map the effects of “metamaterial” lenses in transcranial magnetic stimulation (TMS). The recently developed “metamaterials” can be used to focus electromagnetic fields onto regions that are smaller than the field’s wavelength, previously considered impossible [2]. Oscillating magnetic fields have also been proven useful for targeted drug delivery application [3]. Externally focused magnetic fields of known intensity could possibly improve targeting capabilities of such procedures. Our strategy consists of constructing a forward model for the MRI signal produced by a known object in the presence of an oscillating magnetic field of known temporal characteristics but unknown spatial distribution. We use iterative techniques to estimate the distribution of the field by comparing the measured signal to the signal generated by a forward model. Here, we develop the theoretical framework and illustrate our method by numerical simulations and experimental data. METHODS: Simulations: We simulated a MR image of the Shepp-Logan phantom (32 x 32, FOV = 60 x 60 mm) in the presence of a sinusoidal magnetic field generated by a circular coil (diameter of 4cm, with a current of 5 mA flowing through it) positioned parallel to the slice of interest. For simplicity, the field was simulated using the Biot-Savart equation assuming no additional fields created by charge build up. The field was synchronized with the data acquisition phase of the pulse sequence. Thus, the MR signal s (t) is given by
Causality re-established

Causality has never gained the status of a ‘law’ or ‘principle’ in physics. Some recent literature has even popularized the false idea that causality is a notion that should be banned from theory. Such misconception relies on an alleged universality of the reversibility of the laws of physics, based either on the determinism of classical theory, or on the multiverse interpretation of quantum theory, in both cases motivated by mere interpretational requirements for realism of the theory. Here, I will show that a properly defined unambiguous notion of causality is a theorem of quantum theory, which is also a falsifiable proposition of the theory. Such a notion of causality appeared in the literature within the framework of operational probabilistic theories. It is a genuinely theoretical notion, corresponding to establishing a definite partial order among events, in the same way as we do by using the future causal cone on Minkowski space. The notion of causality is logically completely independent of the misidentified concept of ‘determinism’, and, being a consequence of quantum theory, is ubiquitous in physics. In addition, as classical theory can be regarded as a restriction of quantum theory, causality holds also in the classical case, although the determinism of the theory trivializes it. I then conclude by arguing that causality naturally establishes an arrow of time. This implies that the scenario of the ‘block Universe’ and the connected ‘past hypothesis’ are incompatible with causality, and thus with quantum theory: they are both doomed to remain mere interpretations and, as such, are not falsifiable, similar to the hypothesis of ‘super-determinism’. This article is part of a discussion meeting issue ‘Foundations of quantum mechanics and their impact on contemporary society’.
Causal Consistency for Data Stores and Applications as They are

There have been proposed protocols to achieve causal consistency with a distributed data store that does not make safety guarantees. Such protocols work with an unmodified data store if it is implemented as middleware or a shim layer while it can be implemented inside a data store. But the middleware approach has required modifications to applications. Applications have to explicitly specify data dependency to be managed. Our Letting-It-Be protocol to the contrary, handles all implicit dependency naturally resulting from data accesses even though it is implemented as middleware. Our protocol does not require any modifications to either data stores or applications. It works with them as they are. It trades performance for the merit to some extent. Throughput declines from a data store alone were 21% in the best case and 78% in the worst case without multi-level management of dependency graph, which is a performance optimization technique.
An Ontology-Based Semantic Similarity Computation Model

The semantic similarity computation is very important in many applications that need to process text data. Because ontology can provide explicit concept specification, we establish an ontology-based semantic similarity computation model. This model quantifies the semantic similarity between concepts considering the semantic distance, concept level and the overlapping degree between sets of the hypernyms and hyponyms. The association between the semantic distance and concept level is also presented so that the number of parameters is reduced. The final experiment results demonstrate that this model is effective, and the accuracy is improved without any dependency on corpus.
Ensemble techniques for software change prediction: A preliminary investigation

Predicting the classes more likely to change in the future helps developers to focus on the more critical parts of a software system, with the aim of preventively improving its maintainability. The research community has devoted a lot of effort in the definition of change prediction models, i.e., models exploiting a machine learning classifier to relate a set of independent variables to the change-proneness of classes. Besides the good performances of such models, key results of previous studies highlight how classifiers tend to perform similarly even though they are able to correctly predict the change-proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we aim at analyzing the extent to which ensemble methodologies, i.e., machine learning techniques able to combine multiple classifiers, can improve the performances of change-prediction models. Specifically, we empirically compared the performances of three ensemble techniques (i.e., Boosting, Random Forest, and Bagging) with those of standard machine learning classifiers (i.e., Logistic Regression and Naive Bayes). The study was conducted on eight open source systems and the results showed how ensemble techniques, in some cases, perform better than standard machine learning approaches, even if the differences among them is small. This requires the need of further research aimed at devising effective methodologies to ensemble different classifiers.
An architecture pattern for trusted orchestration in IoT edge clouds

Fog and edge architectures provide a link between centralised clouds and the world of IoT and sensors. The architectures consist of devices of different sizes that coordinate the communication with sensors and cloud services, and that process data from or for the sensors and the cloud locally. In dynamic settings that allow resources to be created, placed, used and removed at any time, advanced orchestration techniques are needed for the activities. Often, these architectures cross organisational boundaries, which causes trust concerns. The origin of data and the identity of sensors or actuators needs to be identified. Additionally, data needs to be stored securely. Orchestration activities across boundaries are subject to a contractual perspective. We present here an architecture pattern that supports trusted orchestration for edge clouds. The W3C Provenance standard is the conceptual foundation of the pattern. Permissioned blockchain technology serves as the platform to implement the provenance framework.
Gated Multi-Task Network for Text Classification

Multi-task learning with Convolutional Neural Network (CNN) has shown great success in many Natural Language Processing (NLP) tasks. This success can be largely attributed to the feature sharing by fusing some layers among tasks. However, most existing approaches just fully or proportionally share the features without distinguishing the helpfulness of them. By that the network would be confused by the helpless even harmful features, generating undesired interference between tasks. In this paper, we introduce gate mechanism into multi-task CNN and propose a new Gated Sharing Unit, which can filter the feature flows between tasks and greatly reduce the interference. Experiments on 9 text classification datasets shows that our approach can learn selection rules automatically and gain a great improvement over strong baselines.
Low-energy key exchange for automation systems

There is a clear trend towards the use of wireless communication in automation systems (AS). To make wireless communication systems usable for automation systems, it is crucial that they fulfil the strong requirements with respect to latency and security. For ensuring security features such as confidentiality and data integrity cipher algorithms are used. In this paper we focus on the most important operation in crypto-systems i.e. the key distribution. As the major part of the devices is resource constraint, energy efficiency is of utmost importance. In this paper we propose to combine the EC ElGamal encryption approach corresponding to [1] with the Montgomery kP algorithm using projective Lopez-Dahab coordinates [4] for realizing the key exchange. The proposed combination helps to reduce the energy needed for exchanging a shared secret key by 30%. This is mainly achieved by reducing the number of bits to be transmitted.
Streaming Graph Partitioning for Large Graphs with Limited Memory

With the graph data scale constantly expanding, the personal computer has brought in severe challenge for the traditional graph partitioning because of its limited memory capacity. The streaming model has been applied in graph partitioning in recent years because it is more efficient than offline partitioning. However, the cache storage structure of original streaming method is inefficient for searching operations on the one hand, on the other hand, the efficiency gradually is reduced with the number of vertices which have been allocated increased because there is no space left for storing more in memory. A caching strategy for streaming algorithm is put forward in this paper including efficient cache storage structure, which uses the vertex and its neighbors’ subset information as basic entry. The cache management module manages cache content. Our method is effective on the condition whether it has limitation to cache capacity or not. By using our cache strategy, it only takes about 25 minutes for partitioning twitter-2010 that have 1.4 billion edges while the original streaming method needs 42 minutes, As can prove the effectiveness and superiority of our method.
Retraining-Based Iterative Weight Quantization for Deep Neural Networks

Model compression has gained a lot of attention due to its ability to reduce hardware resource requirements significantly while maintaining accuracy of DNNs. Model compression is especially useful for memory-intensive recurrent neural networks because smaller memory footprint is crucial not only for reducing storage requirement but also for fast inference operations. Quantization is known to be an effective model compression method and researchers are interested in minimizing the number of bits to represent parameters. In this work, we introduce an iterative technique to apply quantization, presenting high compression ratio without any modifications to the training algorithm. In the proposed technique, weight quantization is followed by retraining the model with full precision weights. We show that iterative retraining generates new sets of weights which can be quantized with decreasing quantization loss at each iteration. We also show that quantization is efficiently able to leverage pruning, another effective model compression method. Implementation issues on combining the two methods are also addressed. Our experimental results demonstrate that an LSTM model using 1-bit quantized weights is sufficient for PTB dataset without any accuracy degradation while previous methods demand at least 2-4 bits for quantized weights.