Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-non-intrusive objective intelligibility measure using spatial filtering in hearing aids

Reliable non-intrusive online assessment of speech intelligibility can play a key role for the functioning of hearing aids, e.g. as guidance for adjusting the hearing aid settings to the environment. While existing intrusive metrics can provide a precise and reliable measure, the current non-intrusive metrics have not been able to achieve acceptable intelligibility predictions. This paper presents a new semi-non-intrusive intelligibility measure based on an existing intrusive measure, STOI, where an estimate of the clean speech is extracted using spatial filtering in the hearing aid. The results indicate that the STOI score obtained with the proposed method using an estimate of the clean speech correlates well with the STOI score having the original clean speech signal available.
VESPA: VIPT Enhancements for Superpage Accesses

L1 caches are critical to the performance of modern computer systems. Their design involves a delicate balance between fast lookups, high hit rates, low access energy, and simplicity of implementation. Unfortunately, constraints imposed by virtual memory make it difficult to satisfy all these attributes today. Specifically, the modern staple of supporting virtual-indexing and physical-tagging (VIPT) for parallel TLB-L1 lookups means that L1 caches are usually grown with greater associativity rather than sets. This compromises performance -- by degrading access times without significantly boosting hit rates -- and increases access energy. We propose VIPT Enhancements for SuperPage Accesses or VESPA in response. VESPA side-steps the traditional problems of VIPT by leveraging the increasing ubiquity of superpages; since superpages have more page offset bits, they can accommodate L1 cache organizations with more sets than baseline pages can. VESPA dynamically adapts to any OS distribution of page sizes to operate L1 caches with good access times, hit rates, and energy, for both single- and multi-threaded workloads. Since the hardware changes are modest, and there are no OS or application changes, VESPA is readily-implementable. By superpages (also called huge or large pages) we refer to any page sizes supported by the architecture bigger than baseline page size.
(Don't) Do It For The Gram: Upward Social Comparison, Self-Discrepancy, and Body Shame Among College-Aged Instagram Users

The aim of this correlational study was to investigate the ways in which engaging in upward social comparison with celebrities and peers on Instagram relates to body shame among 143 undergraduate students. Drawing on Higgins’ (1987) research, the potential moderating roles of ideal and ought self-discrepancies and the related emotions of dejection and agitation were also examined. Overall, findings indicated a statistically significant, positive relationship between peer-targeted upward comparison and body shame (p = .021) as well as celebrity-targeted upward comparison and body shame (p = .002) when controlling for gender, age and race. Evidence failed to support self-discrepancy or gender moderation. In addition, ought selfdiscrepancy, but not ideal self-discrepancy, predicted feelings of agitation (p = .004) and dejection (p = .001). The discussion offers a number of suggestions for future directions in the field of social media and body image research.
Real-time high density people counter using morphological tools

The paper deals with an application of image sequence analysis. In particular, it addresses the problem of determining the number of people who get into and out of a train carriage when it is crowded and background and/or illumination might change. The proposed system analyses image sequences and processes them using an algorithm based on the use of several morphological tools and optical flow motion estimation.
Metagenomic Insights into Metabolic Capacities of the Gut Microbiota in a Fungus-Cultivating Termite (Odontotermes yunnanensis)

Macrotermitinae (fungus-cultivating termites) are major decomposers in tropical and subtropical areas of Asia and Africa. They have specifically evolved mutualistic associations with both a Termitomyces fungi on the nest and a gut microbiota, providing a model system for probing host-microbe interactions. Yet the symbiotic roles of gut microbes residing in its major feeding caste remain largely undefined. Here, by pyrosequencing the whole gut metagenome of adult workers of a fungus-cultivating termite (Odontotermes yunnanensis), we showed that it did harbor a broad set of genes or gene modules encoding carbohydrate-active enzymes (CAZymes) relevant to plant fiber degradation, particularly debranching enzymes and oligosaccharide-processing enzymes. Besides, it also contained a considerable number of genes encoding chitinases and glycoprotein oligosaccharide-processing enzymes for fungal cell wall degradation. To investigate the metabolic divergence of higher termites of different feeding guilds, a SEED subsystem-based gene-centric comparative analysis of the data with that of a previously sequenced wood-feeding Nasutitermes hindgut microbiome was also attempted, revealing that SEED classifications of nitrogen metabolism, and motility and chemotaxis were significantly overrepresented in the wood-feeder hindgut metagenome, while Bacteroidales conjugative transposons and subsystems related to central aromatic compounds metabolism were apparently overrepresented here. This work fills up our gaps in understanding the functional capacities of fungus-cultivating termite gut microbiota, especially their roles in the symbiotic digestion of lignocelluloses and utilization of fungal biomass, both of which greatly add to existing understandings of this peculiar symbiosis.
Detecting Malicious Behavior Using Critical API-Calling Graph Matching

The proliferation of new malware in recent years has presented a serious security threat to our society. Research shows that variants of some known ones take a large amount of new malware, so one of the challenges in malware detection is how to find the similarities between known malware and its variants. Since API(Application Programming Interface)functions is used extensively to achieve the function of one program and It is difficult for different malware versions to conceal similarity on the functional flow level, making use of the similarity of their API-calling sequences is an essential detection method.In this paper, we present a new approach of malware detection based on critical API-calling Graph (CAG) matching rather than considering all API calls. More attention is paid on the illustration of how to extract a CAG from a control flow graph (CFG) for each malware to define a malicious behavior and how to detect the behavior in a suspicious executable using the CAG matching. This approach can overcome the drawbacks of two most common techniques adopted by current antivirus in detection of variants and unknown malware, which is demonstrated by the favorable experimental results.
Manifold regularization for semi-supervised sequential learning

The sequential data flux in many time-series applications require that only a small fraction of the data are stored for future processing. Furthermore, labels for these data are possibly sparse and they might show some biases. To support learning under such restrictive constraints, we combine manifold regularization with sequential learning under a semi-supervised learning scenario. The online learning mechanism integrates a regularization based on the data smoothness assumptions. We present a proof-of-concept for illustrative toy problems, and we apply the algorithm to a real-world sparse online classification task for music categories.
Multiview Image Coding Based on Geometric Prediction

Many existing multiview image/video coding techniques remove inter-viewpoint redundancy by applying disparity compensation in a conventional video coding framework, e.g., H.264/MPEG-4 AVC. However, conventional methodology works ineffectively as it ignores the special characteristics of inter-viewpoint disparity. In this paper, we propose a geometric prediction methodology for accurate disparity vector (DV) prediction, such that we can largely reduce the disparity compensation cost. Based on the new DV predictor, we design a basic framework that can be implemented in most existing multiview image/video coding schemes. We also use state-of-the-art H.264/MPEG-4 AVC as an example to illustrate how the proposed framework can be integrated with conventional video coding algorithms. Our experiments show proposed scheme can effectively tracks disparity and greatly improves coding performance. Compared with H.264/MPEG-4 AVC codec, our scheme outperforms maximally 1.5 dB when encoding some typical multiview image sequences. We also carry out an experiment to evaluate the robustness of our algorithm. The results indicate our method is robust and can be used in practical applications.
3D model-based gaze estimation in natural reading: a systematic error correction procedure based on annotated texts

Studying natural reading and its underlying attention processes requires devices that are able to provide precise measurements of gaze without rendering the reading activity unnatural. In this paper we propose an eye tracking system that can be used to conduct analyses of reading behavior in low constrained experimental settings. The system is designed for dual-camera-based head-mounted eye trackers and allows free head movements and note taking. The system is composed of three different modules. First, a 3D model-based gaze estimation method computes the reader's gaze trajectory. Second, a document image retrieval algorithm is used to recognize document pages and extract annotations. Third, a systematic error correction procedure is used to post-calibrate the system parameters and compensate for spatial drifts. The validation results show that the proposed method is capable of extracting reliable gaze data when reading in low constrained experimental conditions.
PIERNIK mhd code - a multi-fluid, non-ideal extension of the relaxing-TVD scheme (IV)

We present a new multi-fluid, grid MHD code PIERNIK, which is based on the Relaxing TVD scheme (Jin & Xin, 1995). The original scheme (see Trac & Pen (2003) and Pen et al. (2003)) has been extended by an addition of dynamically independent, but interacting fluids: dust and a diffusive cosmic ray gas, described within the fluid approximation, with an option to add other fluids in an easy way. The code has been equipped with shearing-box boundary conditions, and a selfgravity module, Ohmic resistivity module, as well as other facilities which are useful in astrophysical fluid-dynamical simulations. The code is parallelized by means of the MPI library. In this paper we present an extension of PIERNIK, which is designed for simulations of diffusive propagation of the Cosmic-Ray (CR) component in the magnetized ISM.
Distinguishing Lies from Jokes: Theory of Mind Deficits and Discourse Interpretation in Right Hemisphere Brain-Damaged Patients

Right-hemisphere brain damaged (RHD) patients and a normal control group were tested for their ability to infer first- and second-order mental states and to understand the communicative intentions underlying ironic jokes and lies. Subjects listened to stories involving a character who had either a true or a false belief about another character's knowledge. Stories ended either with an ironic joke or a lie by this character. In the joke stories, the speaker knew that the listener knew the truth (a true second-order belief) and did not expect the listener to believe what was said; in the lie stories, the speaker did not know that the listener actually knew the truth (a false second-order belief) and thus did expect the listener to believe what was said. RHD patients performed significantly worse than control subjects on one of two measures of second-order belief, which suggests that the ability to make second-order mental state attributions is fragile and unreliable following right-hemisphere damage. RHD patients in addition performed worse than controls when asked to distinguish lies from jokes, confirming their known difficulties with discourse interpretation. For both groups, the ability to distinguish lies from jokes was strongly correlated with two measures of the ability to attribute correctly second-order beliefs. These results suggest that the fragility of RHD patients' understanding of second-order mental states underlies a portion of their difficulties in discourse comprehension, but that the underlying impairment is not restricted to right hemisphere dysfunction.
Does pre-training reduce the cognitive load of learning complex science information in authentic settings: science classrooms?

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A Secure Data Fusion Algorithm Based on Behavior Trust in Wireless Sensor Networks

Secure data fusion is an important application of wireless sensor network. Previous works are implemented by authentication among neighboring sensors. Considering that in many cases, data fusion event is both spatially and temporally correlated, this paper proposes a distributed and localized secure data fusion algorithm based on behavior trust. The security and reliability of the algorithm are implemented by statistical hypothesis test for matching the reading sequence of sensors and statistical characters of the data fusion event. The simulation results show the validity of the algorithm.
Visualizing Large Telecommunication Data Sets _ _ _ _ _ _ _ _ Visualization Blackboard

mous volume of real-time data. Long-distance voice networks, for example, can complete more than 250 million calls a day; wide-area data networks can support many hundreds of thousands of virtual circuits and millions of Internet protocol (IP) flows and Webserver sessions. Unlike terabyte databases, which typically contain images or multimedia streams, telecommunication databases mainly contain numerous small records describing transactions and network status events. The data processing involved therefore differs markedly, both in the number of records and the data items interpreted. To efficiently configure and operate these networks, as well as manage performance and reliability for the user, these vast data sets must be understandable. Increasingly, visualization proves key to achieving this goal. AT&T Infolab is an interdisciplinary project created in 1996 to explore how software, data management and analysis, and visualization can combine to attack information problems involving large-scale networks. The data Infolab collects daily reaches tens of gigabytes. The Infolab project Swift-3D uses interactive 3D maps with statistical widgets, topology diagrams, and pixeloriented displays to abstract network data and let users interact with it. We have implemented a full-scale Swift3D prototype, which generated the examples we present here.
Shape and appearance knowledge based brain segmentation for neonatal MR images

The neonatal cerebral disorders might deform the brain shape, and reduce the cerebral function. For the diagnosis of cerebral disorders, it is effective to measure cerebral volume and surface area using head magnetic resonance (MR) image. The measurement should require a brain segmentation process. However, there are few studies for neonatal brain. This study proposes a brain segmentation method for a neonatal brain. In this study, we propose a shape and appearance knowledge based brain segmentation (SABS) method. SABS method segments a brain and cerebrospinal fluid (CSF) region by using a brain atlas model. Next, it classifies a brain and CSF region into some classes by using Bayesian classification with Gaussian mixture model, and optimizes the brain surface by using fuzzy rule-based active surface model method. Experimental results in 14 neonatal subjects (revised age between −1 month and 1 month) showed that the proposed method segmented the brain region with higher accuracy than the conventional methods.
An Embedded Saliency Map Estimator Scheme: Application to Video Encoding

In this paper we propose a novel saliency-based computational model for visual attention. This model processes both top-down (goal directed) and bottom-up information. Processing in the top-down channel creates the so called skin conspicuity map and emulates the visual search for human faces performed by humans. This is clearly a goal directed task but is generic enough to be context independent. Processing in the bottom-up information channel follows the principles set by Itti et al. but it deviates from them by computing the orientation, intensity and color conspicuity maps within a unified multi-resolution framework based on wavelet subband analysis. In particular, we apply a wavelet based approach for efficient computation of the topographic feature maps. Given that wavelets and multiresolution theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our implementation goes further. We utilize the wavelet decomposition for inline computation of the features (such as orientation angles) that are used to create the topographic feature maps. The bottom-up topographic feature maps and the top-down skin conspicuity map are then combined through a sigmoid function to produce the final saliency map. A prototype of the proposed model was realized through the TMDSDMK642-0E DSP platform as an embedded system allowing real-time operation. For evaluation purposes, in terms of perceived visual quality and video compression improvement, a ROI-based video compression setup was followed. Extended experiments concerning both MPEG-1 as well as low bit-rate MPEG-4 video encoding were conducted showing significant improvement in video compression efficiency without perceived deterioration in visual quality.
Object recognition based on adapative bag of feature and discriminative learning

In this paper, a new method is proposed to incorporate the saliency map to weight the extracted features with discriminative technique for learning the spatial discriminative information of images. Different from the conventional bag of word (BoW) approach, the descriptive bag of phrase approach is explored to capture the word co-occurrence and dependence. The image score based on the saliency map is learned to optimize the support vector machine (SVM) parameter. Discriminative learning techniques are adopted based on image score and fed into the SVM classifier. Moreover, the histogram intersection mapping and normalization method is further adopted to enhance the classification performance. Experimental results on the 3 popular databases demonstrate the effectiveness of the method and show the promising performance over the existing state-of-the-art methods.
Selected Technical Aspects of Tu-154M Smolensk Air Crash on April 10, 2010

This is a collection of reports that consists of three parts. The author is a Professor of PowerElectrical Engineering, so he focuses on the Tu-154M power electric system and all aspects of the air crashthat relate to electrical equipment and wiring. Part I discusses the electric power system of the Tu-154M. After brief introduction to aircraft power systems,the results of reverse design and analysis of GT40PCh6 wound-eld synchronous generator including shortcircuit have been presented. An example of failure of GT40PCh6 generator is the re of the Tu-154B-2 onJanuary 1, 2011 before taking o at Surgut airport (ight 7K348). Guidelines for proper investigation ofaircraft electric equipment and wiring after crash have been given. There is no evidence of examinationof most electrical equipment of the Tu-154M No 101 after crash on April 10, 2010. It is now extremelydicult to determine, if the electric power system of the Tu-154M No 101 was operating correctly in thelast seconds of the ight, or not. Part II analyzes the fuel system and possibility of explosion of fuel-air mixture as a result of arcing and/orstatic electricity in the left wing outer fuel tank of the Tu-154M No 101. Examples of explosions of fueltanks (Boeing 747-131 TWA 800 on June 17, 1996 and Boeing 727-200 at Bangalore Airport on May 42006) have been discussed. Although probability of explosion of fuel in the left wing outer tank due theelectric short circuit, arcing or static electricity is low, this problem should be carefully considered in futureexamination of the wreckage and remaining electrical wiring and equipment. Part III describes a comparative analysis of hypothetical collision of the Tu-154M No 101 with birch tree,full-scale dynamic crash test of Douglas DC-7 and full-scale dynamic crash test of Lockheed Constellation1649. The analysis pertains to the technical data of the Tu-154M, DC-7 and LC-1649 airliners, dierencesin their construction and conditions of collision/impact.
Robust Sparsity-Based Device-Free Passive Localization in Wireless Networks

As an emerging technique with a promising application prospect, the device-free passive localization (DFPL) technique has drawn considerable research efiorts due to its ability of realizing wireless localization without the need of carrying any device and participating actively in the localization process. Recent technological achievements of the DFPL technique have made it feasible to realize location estimation using the received signal strength (RSS) information of wireless links. However, one major disadvantage of the RSS-based DFPL technique is that the RSS measurement is too sensitive to noise and environmental variations, which incur the misjudgment of shadowed links and degradation of localization performance. Based on the natural sparsity of location flnding in the spatial domain, this paper proposes an environmental-adaptive sparsity-based localization method for the DFPL problem in the existence of model mismatch. The novel feature of this method is to adjust both the overcomplete basis (a.k.a. dictionary) and the sparse solution using a dictionary learning (DL) technique based on the quadratic programming approach so that the location solution can better match the changes of the RSS measurements between the node pairs to the spatial location of the target. Moreover, we propose a modifled re-weighting l1 norm minimization algorithm to improve reconstruction performance for sparse signals. The efiectiveness of the proposed scheme is demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.