Miracle-dz/UniFAR-FTUs huggingface dataset:
A memory-centric SIMD neural network accelerator:balancing efficiency & flexibility

Nowadays, cameras are everywhere. These cameras can be used to secure properties, as safety for road users, marketing and many other reasons. To perform these tasks, it is required to have object recognition. Object recognition is required at the camera to reduce network dependability and therefore energy. Since the same camera can be used to detect various object, it is required to have a general approach for object recognition. Convolutional Neural Networks (CNNs) offer this general approach. The advantage of CNNs is that they can be easily learned to detect objects and are flexible to support different kind of vision tasks. Running different CNN configurations on an embedded platform is a challenging task. General Purpose processors can not meet the throughput requirement within realistic power constraints, and the known dedicated CNN accelerators do not have the required flexibility. To overcome the limitations of the current state of the art accelerators, a flexible, clustered, Single Instruction, Multiple Data (SIMD) accelerator is designed. The SIMD paradigm is used to efficiently exploit available parallelism in the algorithm. A specialized memory hierarchy is used to reduce the enormous data transfer rates to external memory. To maximize utilization for the varying work load in a CNN, a dedicated optimizing toolflow is developed. The evaluation shows that the proposed solution can achieve 7.4 Giga Operations Per Second (GOPS) with a utilisation of 57% of the available Processing Elements (PEs). Due to the memory hierarchy the required external bandwidth is reduced from 97.5GB/sec to 400MB/sec, for these benchmarks. The power consumption of mapping to the ZedBoard[1] indicates a reduction of 3−30× compared with a GPU implementation, and 6 − 52× compared with a CPU implementation. The result of this work is a very flexible platform for CNN based vision applications that achieve state-of-the-art performance in throughput and energy.
Deep fusion prior for plenoptic super-resolution all-in-focus imaging

Abstract. Plenoptic imaging offers not only two-dimensional projections but also adds light array directions, thus supporting single-shot all-in-focus imaging. Its poor spatial resolution becomes an obstacle to high-quality all-in-focus imaging performance. Although various super-resolution (SR) methods have been designed and combined with multifocus image fusion (MFIF), high-quality multifocus fused SR images can be reconstructed for various applications, almost all of them deal with MFIF and SR separately. To our best knowledge, we first unify MFIF and SR problems as the multifocus image SR fusion (MFISRF) in the optical perspective and thus propose a dataset-free unsupervised framework named deep fusion prior (DFP) to address such MFISRF, particularly for plenoptic SR all-in-focus imaging. Both numerical and practical experiments have proved that our proposed DFP approaches or even outperforms those state-of-the-art MFIF and SR method combinations. Therefore, we believe DFP can be potentially used in various computational photography applications. The DFP codes are open source and available at http://github.com/GuYuanjie/DeepFusionPrior.
Transformation de la prosodie par adaptation MLLR de GMM

In a voice transformation context, prosody transformation using parallel corpora is quite unrealistic as such corpora are difficult and also expensive to build. Based on this observation, we propose an approach for transforming prosody using non-parallel corpora thanks to the MLLR adaptation strategy. This methodology is applied to the joint transformation of duration and F0 at the syllable level. The source data are modelled by a GMM which is adapted to the target by applying a linear transformation to the mean vectors of the gaussian mixture. This methodology is applied to the conversion of duration and F0 between two french speakers and is evaluated by cross validation between the models and the test datasets.
Fire, Tractors, and Health in the Amazon: A Cost-Benefit Analysis of Fire Policy

Pollution from agricultural fires is a global health issue that is particularly challenging where smallholders depend on burnings for subsistence. In Acre state, Western Amazon, a partial ban on fire, enforced with fines, is coupled with subsidized tractors. To evaluate this policy, a discrete choice experiment and contingent valuation were merged into a novel statistical variant of the Hicks-Kaldor test that is robust to preference heterogeneity. Among 27 ways to extend the ban, 5 could improve both respiratory health and smallholders’ welfare, when compensated with tractors that are available for longer hours and at the right time of the year. (JEL Q51, Q52)
A New Hybrid Method for VoIP Stream Steganography

In this article, a new stream steganography method for real time digital data network environment with the focus on VoIP is presented. This method consists of a combination of Least Significant Bits (LSB) algorithm and an algorithm called LACK, which is based on the delayed packets in VoIP streams, and forms a new algorithm called LASB (LACK-LSB combination). The combination allows both the reduction of detection probability of stegano data and the use of LSB high capacity as well as being more robust because of diversifying the source of data embedding. That is, by combing these two methods the probability of detection existing steganography data is reduced extensively. Moreover, in the case of detecting stegano data, the potential eavesdropper is not able to easily reconstruct it. To show the performance, both LACK and LSB algorithms are implemented and are compared with the new LASB algorithm in the VoIP environment in terms of capacity, eavesdropping and robustness under several attacks. The experimental results show that LASB is superior to both LACK and LSB in many aspects.
Surface Nanoscale Axial Photonics (SNAP) for Optofluidics

Sensing with optical whispering gallery modes (WGMs) is a rapidly developing detection method in modern microfluidics research. This method explores the perturbations of spectra of WGMs propagating along the wall of an optical microresonator to characterize the liquid medium inside it. Out of the many available types of WGM microresonators, the surface nanoscale axial photonics (SNAP) platform enables fabrication of resonant ultralow loss photonics structures at the surface of an optical fiber with unprecedented precision currently approaching 0.1 angstroms. In this work, first we explore a new technique for the creation of SNAPs, by using a regular hydrogen-oxygen torch, which requires less equipment than current techniques. The transmission spectra shows that light can be fully localized by pulling a fiber, with very low loss resonant modes. We then present the first demonstration of a platform with potential for microfluidic sensing based on SNAP microresonators fabricated in silica capillary fiber with ultra-thin walls by local annealing with a focused CO2 laser and internal etching with hydrofluoric acid. This demonstration paves the groundwork for advanced microfluidic sensing with SNAP microresonators. Finally, we show that light circulating in a silica microcapillary can be fully localized by evanescent coupling to a water droplet forming a high Q-factor microresonator. The discovered phenomenon suggests a novel method for microfluidics sensing and a new type of tunable resonant microfluidic-based photonic devices.
A Literature Survey on Learning a Propagable Graph for Classification under the Scenario with Uncertain Labels

In this paper, we present that Learning by propagability is a novel feature extraction framework. The entire leaning methodology is based on the data labels and optimality of feature representation that can create a harmonic system. Here data labels are invariant regarding the propagation on the similarity graph constructed based on the optimal feature representation. Using this idea, we can perform classification. This approach deals with multiclass classification problems. Dealing with uncertain labels is the crucial problem in today's world. Here, we have tried to explain theory of belief functions which is used to treat uncertain labels. Applying two or more learning rules, we can implement the classification procedure for uncertain labels data sets. IndexTerms—Semi-supervised learning, graph-based learning, propagability, graph harmoniousness, uncertainty, feature extraction, multiclass classification
Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network

The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic features from image sequences that represent the proportions of euclidean distances between facial fiducial points in the first frame, and faicial fiducial points in the last frame. Secondly, a feature selection method is used to select only the most relevant features from them. Finally, the selected features are presented to a Neural Network (NN) classifier to classify facial expression input into emotion. The proposed approach has achieved an emotion recognition accuracy of 99% on the CK+ database, 84.7% on the Oulu-CASIA VIS database, and 93.8% on the JAFFE database.
The effect of explicit vocabulary instruction using specialized graphic organizers in secondary mathematics for students with disabilities

ii DEDICATION . iii ACKNOWLEDGEMENTS . iv LIST OF TABLES . xi LIST OF FIGURES . xii CHAPTER I: INTRODUCTION . 1 Academic Vocabulary Instruction . 2 Instructional Practices in Mathematics Using Specialized Graphic Organizers . 5 Statement of the Problem . 6 Purpose of the Study . 7 Research Questions . 8 Definition of Terms. 9 Summary . 10 CHAPTER II: REVIEW OF LITERATURE . 11 Content Area Literacy . 13 Discipline Specific Literacy . 13 Procedural vs. Conceptual Instruction and Levels of Understanding in Mathematics . 14 Instructional Routines for Teaching Vocabulary . 16 Scaffolded Instruction . 16 Morphological Instruction . 17
Queue-munity engagement: Collaborative Event Ethnography at the Antiques Roadshow in Kent

Using Collaborative Event Ethnography as a research method, a team of 21 researchers conducted fieldwork at the Antiques Roadshow in Ightham Mote, Kent. This article reflects on the experience of queuing at the event and how it was experienced and discussed by researchers and participants. Drawing upon Mol, the article approaches the practice of queuing as involving an inherent multiplicity of often contradictory experiences through which idealised, Second World War-related understandings of British queuing practice are simultaneously confirmed and challenged. Through multiple participants viewing the queue from within and outside we were able to capture the processes of community-building, curation, management, rule-maintenance and rule-bending within the social life of the queue. In demonstrating this multiplicity Collaborative Event Ethnography is shown to be an excellent research tool for capturing short-term, large-scale events while also highlighting the simultaneous simplicity and complexity of social interactions and social norms within queues.
Yöneticilerin Öz Yeterlilik İnançları, Yönetsel Güçlülük Düzeyleri ve Karar Verme Stilleri Arasındaki İlişki (Relationship Between Self Efficacy Managerial Resourcefulness and Decision Making Styles of Managers)

Yöntem – Bu doğrultuda öz yeterlilik ve yönetsel güçlülük düzeyinin karar verme stillerine etkisi belirlemeye yönelik model oluşturulmuş ve analizler gerçekleştirilmiştir. Analizler esnasında regresyon analizi ve Sobel testinden faydalanılmıştır. Araştırmanın bağımsız değişkenini öz yeterlilik, aracı değişkenini yönetsel güçlülük kavramı, bağımlı değişkenini ise karar verme stilleri oluşturmuştur. Çalışmada veri toplamak için Magaletta ve Oliver (1999) tarafından geliştirilen Yıldırım ve İlhan (2010) tarafından geçerlilik ve güvenirlik analizleri yapılan Öz Yeterlilik Ölçeğinden, Kanungo ve Menon (2005) tarafından geliştirilen Yönetsel Güçlülük ölçeğinden, Scott ve Bruce (1995) tarafından geliştirilen karar verme stilleri ölçeğinden faydalanılmıştır. Çalışmada evrenin bütününe ulaşma şansı olduğu için örnekleme gidilmemiş evrenin tümünden veri elde edilmiştir. Alan araştırması kapsamında Nevşehir il ve ilçelerinde yer alan dört ve beş yıldızlı otellerde görev yapan tepe yöneticileri ve kısım yöneticilerine anket uygulaması gerçekleştirilmiştir.
Signature Verification Using Morphological Features Based on Artificial Neural Network

For identification of a particular human being signatures prove to be an important biometric. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this paper, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified cbn based on parameters extracted from the signature using various image processing techniques. This paper presents a proposed method for verifying offline-signatures . Novel features are used for classification of signatures. A Feed Forward Neural Network will be used for verifying signatures and to determine its accuracy.
Modernizing Storage Device Interface for Performance and Reliability

Modern solid state drives (SSDs) unnecessarily confine applications to the conventional block I/O interface, which under-utilizes SSD’s internal resources, leading to suboptimal performance and unsatisfactory lifetime.This thesis first presents the key-addressable, multi-log SSD (KAML), an SSD with a key-value interface that uses a novel multi-log architecture and stores data as variable-sized records rather than fixed-sized sectors. Exposing a key-value interface allows applications to remove a layer of indirection between application-level keys and data stored in the SSD. KAML also provides native support for fine-grained atomicity and isolation. We have implemented a prototype of KAML on an SSD development platform, and results show that KAML outperforms conventional systems by up to 4.0X. Existing SSDs also provide flash-based out-of-band (OOB) data that can only be updated on a conventional write. Consequently, the metadata stored in their OOB region lack flexibility due to the idiosyncrasies of flash memory, incurring unnecessary flash write operations detrimental to device lifetime. This thesis also presents PebbleSSD, an SSD with byte-addressable metadata, or BAM, as a mechanism exploiting the nonvolatile, byte-addressable random access memory (NVRAM) inside the SSD. With BAM, PebbleSSD can support a range of useful features to improve its lifetime by reducing redundant flash writes. We have implemented a prototype of PebbleSSD on an SSD development platform, and experimental results demonstrate that PebbleSSD can reduce the amount of data written by log-structured file systems during log cleaning by up to 99%, and reduce file-system-level write amplification by up to 33% for a number of workloads.Finally, previous proposals for SSDs with new interfaces suffer from the limitation caused by one-at-a-time design approach. To overcome this limitation, the thesis presents Willow, a user-programmable SSD with programmability as a central feature. Willow allows programmers to augment and extend the semantics of an SSD with application-specific features without compromising file system protections. We demonstrate the effectiveness and flexibility of Willow by implementing support for atomicity as an example. We find that defining SSD semantics in software is easy and beneficial, and that Willow makes it feasible for database transaction processing workload to benefit from a customized SSD interface.
Cross-Age Connections: The Effects of a Peer Mentoring Program on Students’ Connectedness to Self, Others and School, in Two Public Elementary Schools in the American West

The purpose of this research was to determine if participation in a cross-age, peer mentoring program affected student connectedness to self, others and school. The research took place at two public elementary schools in the western United States. Eighty-three students in grades K-1 and 4-6 participated. Data on student connectedness was collected before and after implementation of the mentoring program using multiple measures including student selfassessments, student and teacher prompted journals, observational field notes collected by researchers and attendance. Participation in the mentoring program was found to increase student connectedness to self, others and school for both mentees and mentors. A mentoring program proved to be an easily implemented and effective intervention to encourage selfactualization in students. Tools such as peer mentoring programs that drive students to reach their highest potentials are recommended as courses of action for all schools.
Natural Image and Video Decomposition with Applications to Single Image Denoising

— Rain, fog and Gaussian noise removal from an image and video is a challenging problem and has been recently investigated extensively. In this paper, present a self-learning based image and video decomposition framework. Based on sparse representation, this method ﬁrst learns an over-complete dictionary from the high spatial frequency parts of the input image for reconstruction purposes. An unsupervised clustering on the observed dictionary atoms has been performed in this work. And their corresponding reconstructed image versions via, afﬁnity propagation, which allows to identify image-dependent components with similar context information. While applying this method for the applications of image and video denoising, it was able to automatically determine the undesirable patterns like rain streaks, Fog or Gaussian noise. From the derived image components directly from the input image or video, so that the task of single-image denoising can be addressed. DWT is used for better performance. Fog will degrade the quality of the preview image by reducing the saturation and contrast. The objective of this method is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. Here single frame is used for enhancing foggy images using multi-level transmission map. The method is fast and free from noise. Comparison with the existing method shows that this method provides better processing time and quality. Experiments were conducted based on: single-image and video denoising with Gaussian, Fog and rain noises. The empirical results conﬁrm the effectiveness and robustness of this approach, which is proved to outperform state-of-the-art image denoising algorithms.
Exploring and Enhancing the Recent Trends and Applications in the Energy Sector
of Uganda: a Legal Perspective

This paper reviews the energy sector of Uganda, which is characterized by excessive use of Biomass to provide for over 90% of the energy needs, Uganda being one of the least developed countries in the East African Region. Hydropower provides over 90% of the electricity generated in Uganda while solar, geothermal and other energy sources are underdeveloped. The legal regime and the energy policies are geared towards the use of modern, and clean energy efficient technologies. Various institutions are important in ensuring sustainability in the energy sector of Uganda. This is in form of stimulating Public Private Partnership, attracting multilateral and bilateral agencies to provide funding, grants, and technical assistance in renewable energy projects. The Clean Development Mechanisms of the United Nations avail project developers the opportunity to obtain carbon finance. Several opportunities in Uganda’s energy sector have been highlighted. Equally, several challenges hindering the development of the energy sector and utilization of renewable energy resources have been identified. Finally, conclusion and recommendations for proper management of Uganda’s energy sector have been suggested.
Improved Statistical Language Model for Context-sensitive Spelling Error Candidates

The performance of the statistical context-sensitive spelling error correction depends on the quality and quantity of the data for statistical language model. In general, the size and quality of data in a statistical language model are proportional. However, as the amount of data increases, the processing speed becomes slower and storage space also takes up a lot. We suggest the improved statistical language model to solve this problem. And we propose an effective spelling error candidate generation method based on a new statistical language model. The proposed statistical model and the correction method based on it improve the performance of the spelling error correction and processing speed.
Inequality in Brazil: A Regional Perspective

In this study, we document the decline in income inequality and a convergence in consumption patterns in Brazilian states in a new database constructed from micro data from the national households’ survey. We adjust the state-Gini coefficients for spatial price differences using information on households’ rental prices available in the survey. In a panel regression framework, we find that labor income growth, formalization, and schooling contributed to the decline in inequality during 2004-14, but redistributive policies, such as Bolsa Familia, have also played a positive role. Going forward, it will be important to phase out untargeted subsidies, such as public spending on tertiary education, and contain growth of public sector wages, to improve budgetary efficiency and protect gains in equality.
The State of Strategic Marketing Application in Czech Small and Medium-Sized Enterprises

The article deals with the issue of using strategic marketing in the business of small and medium-sized enterprises. The main objective of this paper is to theoretically define the principles of strategic marketing and then practically analyse the level of strategic marketing use as a tool of business management in selected Czech small and medium-sized enterprises. In order to fulfil the research plan, secondary and primary research was carried out. The theoretical part defines the basic problem areas such as various approaches to strategic marketing defining and strategic management process versus strategic marketing process. In the practical part, 335 companies are analysed, the results are commented and hypotheses evaluated. The most important findings are generally low rate of utilization of strategic marketing management tools and the utilization of these tools is dependent on company size, with micro companies being the worst and medium companies the best.