nils-herrmann/clean_scirepeval huggingface dataset:
A knowledge chain management framework to support integrated decisions in global supply chains

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Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA

Binarized Neural Network (BNN) removes bitwidth redundancy in classical CNN by using a single bit (-1/+1) for network parameters and intermediate representations, which has greatly reduced the off-chip data transfer and storage overhead. However, a large amount of computation redundancy still exists in BNN inference. By analyzing local properties of images and the learned BNN kernel weights, we observe an average of  ~78% input similarity and  ~59% weight similarity among weight kernels, measured by our proposed metric in common network architectures. Thus there does exist redundancy that can be exploited to further reduce the amount of on-chip computations.
 Motivated by the observation, in this paper, we proposed two types of fast and energy-efficient architectures for BNN inference. We also provide analysis and insights to pick the better strategy of these two for different datasets and network models. By reusing the results from previous computation, much cycles for data buffer access and computations can be skipped. By experiments, we demonstrate that 80% of the computation and 40% of the buffer access can be skipped by exploiting BNN similarity. Thus, our design can achieve 17% reduction in total power consumption, 54% reduction in on-chip power consumption and 2.4× maximum speedup, compared to the baseline without applying our reuse technique. Our design also shows 1.9× more area-efficiency compared to state-of-the-art BNN inference design. We believe our deployment of BNN on FPGA leads to a promising future of running deep learning models on mobile devices.
The effect of motor action and different sensory modalities on terrain classification in a quadruped robot running with multiple gaits

Discriminating or classifying different terrains is an important ability for every autonomousmobile robot. A variety of sensors, preprocessing techniques, and algorithms in different robots were applied. However, little attentionwas paid to theway sensory datawas generated and to the contribution of different sensory modalities. In this work, a quadruped robot traversing different grounds using a variety of gaits is used, equipped with a collection of proprioceptive (encoders on active, and passive compliant joints), inertial, and foot pressure sensors. The effect of different gaits on classification performance is assessed and it is demonstrated that separate terrain classifiers for eachmotor program should be employed. Furthermore, poor performance of randomly generated motor commands confirms the importance of coordinated behavior on sensory information structuring. The collection of sensors sensitive to active, ‘‘tactile’’, terrain exploration proved effective. Among the individual modalities, encoders on passive compliant joints delivered best results.
Psycho-education in bipolar disorder: effect on expressed emotion

In a waiting-list controlled study on a multi-family psycho-educational intervention in bipolar disorder, key relatives in the treatment group showed a significant change from high to low levels of expressed emotion (EE) compared with the control group. In addition, patients with low-EE key relatives had a significantly lower number of hospital admissions compared with those living with high-EE key relatives. The multi-family groups were well received by the participants, and there were only a few drop-outs.
First year student performance in a test for computational thinking

Computational thinking, a form of thinking and problem solving within computer science, has become a popular focus of research on computer science education. In this paper, we attempt to investigate the role that computational thinking plays in the experience of introductory computer science students at a South African university. To this end, we have designed and administered a test for computational thinking ability, and contrasted the results of this test with the class marks for the students involved. The results of this test give us an initial view of the abilities that students possess when entering the computer science course. The results indicate that students who performed well in the assessment have a favourable pass rate for their class tests, and specific areas of weakness have been identified. Finally, we describe the plan for a follow-up test to take place at the end of the course to determine how students' abilities have changed over a semester of studies.
Discovering key moments in social media streams

This paper introduces a general technique, called LABurst, for identifying key moments, or moments of high impact, in social media streams without the need for domain-specific information or seed keywords. We leverage machine learning to model temporal patterns around bursts in Twitter's unfiltered public sample stream and build a classifier to identify tokens experiencing these bursts. We show LABurst performs competitively with existing burst detection techniques while simultaneously providing insight into and detection of unanticipated moments. To demonstrate our approach's potential, we compare two baseline event-detection algorithms with our language-agnostic algorithm to detect key moments across three major sporting competitions: 2013  Super Bowl, and 2014 World Cup. Our results show LABurst outperforms a time series analysis baseline and is competitive with a domain-specific baseline even though we operate without any domain knowledge. We then go further by transferring LABurst's models learned in the sports domain to the task of identifying earthquakes in Japan and show our method detects large spikes in earthquake-related tokens within two minutes of the actual event.
SpecGuard: Spectrum misuse detection in dynamic spectrum access systems

Dynamic spectrum access is the key to solving worldwide spectrum shortage. The open wireless medium subjects DSA systems to unauthorized spectrum use by illegitimate users. This paper presents SpecGuard, the first crowdsourced spectrum misuse detection framework for DSA systems. In SpecGuard, a transmitter is required to embed a spectrum permit into its physical-layer signals, which can be decoded and verified by ubiquitous mobile users. We propose three novel schemes for embedding and detecting a spectrum permit at the physical layer. Detailed theoretical analyses, MATLAB simulations, and USRP experiments confirm that our schemes can achieve correct, low-intrusive, and fast spectrum misuse detection.
Retinal expression of the serine protease matriptase-2 (Tmprss6) and its role in retinal iron homeostasis

Matriptase-2 (also known as TMPRSS6) is a critical regulator of the iron-regulatory hormone hepcidin in the liver; matriptase-2 cleaves membrane-bound hemojuvelin and consequently alters bone morphogenetic protein (BMP) signaling. Hemojuvelin and hepcidin are expressed in the retina and play a critical role in retinal iron homeostasis. However, no information on the expression and function of matriptase-2 in the retina is available. The purpose of the present study was to examine the retinal expression of matriptase-2 and its role in retinal iron homeostasis.



RT-PCR, quantitative PCR (qPCR), and immunofluorescence were used to analyze the expression of matriptase-2 and other iron-regulatory proteins in the mouse retina. Polarized localization of matriptase-2 in the RPE was evaluated using markers for the apical and basolateral membranes. Morphometric analysis of retinas from wild-type and matriptase-2 knockout (Tmprss6(msk/msk) ) mice was also performed. Retinal iron status in Tmprss6(msk/msk) mice was evaluated by comparing the expression levels of ferritin and transferrin receptor 1 between wild-type and knockout mice. BMP signaling was monitored by the phosphorylation status of Smads1/5/8 and expression levels of Id1 while interleukin-6 signaling was monitored by the phosphorylation status of STAT3.



Matriptase-2 is expressed in the mouse retina with expression detectable in all retinal cell types. Expression of matriptase-2 is restricted to the apical membrane in the RPE where hemojuvelin, the substrate for matriptase-2, is also present. There is no marked difference in retinal morphology between wild-type mice and Tmprss6(msk/msk) mice, except minor differences in specific retinal layers. The knockout mouse retina is iron-deficient, demonstrable by downregulation of the iron-storage protein ferritin and upregulation of transferrin receptor 1 involved in iron uptake. Hepcidin is upregulated in Tmprss6(msk/msk) mouse retinas, particularly in the neural retina. BMP signaling is downregulated while interleukin-6 signaling is upregulated in Tmprss6(msk/msk) mouse retinas, suggesting that the upregulaton of hepcidin in knockout mouse retinas occurs through interleukin-6 signaling and not through BMP signaling.



The iron-regulatory serine protease matriptase-2 is expressed in the retina, and absence of this enzyme leads to iron deficiency and increased expression of hemojuvelin and hepcidin in the retina. The upregulation of hepcidin expression in Tmprss6(msk/msk) mouse retinas does not occur via BMP signaling but likely via the proinflammatory cytokine interleukin-6. We conclude that matriptase-2 is a critical participant in retinal iron homeostasis.
Lenses for Circular Polarization Using Planar Arrays of Rotated Passive Elements

A planar array of passive lens elements can be phased to approximate the effect of a curved dielectric lens. The rotational orientation of each element can provide the required phase shift for circular polarization. The array elements must be designed so that the hand of circular polarization changes as the electromagnetic wave passes through the lens. An element is presented that is based on an aperture-coupled microstrip patch antenna, and two lenses are designed. Each lens has a diameter of 254 mm and contains 349 elements. The elements have identical dimensions but the rotational orientation of each element is selected to provide a specific lens function. The first lens is designed to collimate radiation from a feed horn into a beam pointing 20° from broadside. At 12.9 GHz the aperture efficiency is 48%. The second lens acts as a Wollaston-type prism. It splits an incident wave according to its circular polarization components.
The Use of Prognostic Health Management for Autonomous Unmanned Air Systems

Unmanned Air Systems (UAS) show great promise for a range of civilian applications, especially „dull, dirty or dangerous‟ missions such as air-sea rescue, coastal and border surveillance, fisheries protection and disaster relief. As the demand for autonomy increases, the importance of correctly identifying and responding to faults becomes more apparent, as fully autonomous systems must base their decisions solely upon the sensors readings they receive – as there is no human on board. A UAS must be capable of performing all the functions that would be expected from a human pilot, including reasoning about faults and making decisions about how to best mitigate their consequences, given the larger context of the overall mission. As these autonomous techniques are developed their benefits can also be realised in non-autonomous systems, as realtime aids to human operators or crew. This paper proposes a novel approach to PHM that combines advanced Functional Failure Mode Analysis with a reasoning system, to provide effective PHM for autonomous systems and improved diagnosis capability for manned aircraft.
Routing automated guided vehicles in container terminals through the Q-learning technique

This paper suggests a routing method for automated guided vehicles in port terminals that uses the Q-learning technique. One of the most important issues for the efficient operation of an automated guided vehicle system is to find shortest routes for the vehicles. In this paper, we determine shortest-time routes inclusive of the expected waiting times instead of simple shortest-distance routes, which are usually used in practice. For the determination of the total travel time, the waiting time must be estimated accurately. This study proposes a method for estimating for each vehicle the waiting time that results from the interferences among vehicles during travelling. The estimation of the waiting times is achieved by using the Q-learning technique and by constructing the shortesttime routing matrix for each given set of positions of quay cranes. An experiment was performed to evaluate the performance of the learning algorithm and to compare the performance of the learning-based routes with that of the shortest-distance routes by a simulation study.
Real world smart chatbot for customer care using a software as a service (SaaS) architecture

It's being very important to listen to social media streams whether it's Twitter, Facebook, Messenger, LinkedIn, email or even company own application. As many customers may be using this streams to reach out to company because they need help. The company have setup social marketing team to monitor this stream. But due to huge volumes of users it's very difficult to analyses each and every social message and take a relevant action to solve users grievances, which lead to many unsatisfied customers or may even lose a customer. This papers proposes a system architecture which will try to overcome the above shortcoming by analyzing messages of each ejabberd users to check whether it's actionable or not. If it's actionable then an automated Chatbot will initiates conversation with that user and help the user to resolve the issue by providing a human way interactions using LUIS and cognitive services. To provide a highly robust, scalable and extensible architecture, this system is implemented on AWS public cloud.
Planning Problems in Intermodal Freight Transport : Accomplishments and Prospects

Intermodal freight transport has received an increased attention due to problems of road congestion, environmental concerns and traffic safety. A growing recognition of the strategic importance of speed and agility in the supply chain is forcing firms to reconsider traditional logistic services. As a consequence, research interest in intermodal freight transportation problems is growing. This paper provides an overview of planning decisions in intermodal freight transport and solution methods proposed in scientific literature. Planning problems are classified according to type of decision maker and decision level. General conclusions are given and subjects for further research are identified.
Multivariate time series prediction of lane changing behavior using deep neural network

Many real world pattern classification problems involve the process and analysis of multiple variables in temporal domain. This type of problem is referred to as Multivariate Time Series (MTS) problem. It remains a challenging problem due to the nature of time series data: high dimensionality, large data size and updating continuously. In this paper, we use three types of physiological signals from the driver to predict lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver’s response to the driving environment. A novel Group-wise Convolutional Neural Network, MTS-GCNN model is proposed for MTS pattern classification. In our MTS-GCNN model, we present a new structure learning algorithm in training stage. The algorithm exploits the covariance structure over multiple time series to partition input volume into groups, then learns the MTS-GCNN structure explicitly by clustering input sequences with spectral clustering. Different from other feature-based classification approaches, our MTS-GCNN can select and extract the suitable internal structure to generate temporal and spatial features automatically by using convolution and down-sample operations. The experimental results showed that, in comparison to other state-of-the-art models, our MTS-GCNN performs significantly better in terms of prediction accuracy.
Semantic , Lexical , and Geographic Cues are used in Geographic Fluency

Semantic fluency tasks have increasingly been used to probe the structure of human memory, adopting methodologies from the ecological foraging literature to describe memory as a trajectory through semantic space. Clusters of semantically related items are often produced together, and the transitions between these clusters of semantically related items are consistent with theories of optimal foraging, where the search process exhibits a balance between exploration and exploitation behaviors (Hills, Jones, & Todd, 2012). Here, we use a semantic fluency memory task in which subjects recall geographic locations. For each pairwise transition, we measure temporal, geographic, semantic, lexical, and phonetic distances. In general, the dimensions are loosely but reliably correlated with each other. Segmentation of the retrieval sequence into patches supports the notion that subjects strategically leave patches as within-patch resources diminish, but also suggests that subjects may shift their attention between different sources of information, perhaps reflecting dynamically changing patch definitions.
Lip reading techniques: A survey

Automated lip reading is the process of converting movements of the lips, face and tongue to speech in real time with enhanced accuracy. Although performance of lip reading systems is still not remotely similar to audio speech recognition, recent developments in processor technology and the massive explosion and ubiquity of computing devices accompanied with increased research in this field has reduced the ambiguities of the labial language, making it possible for free speech-to-text conversion. This paper surveys the field of lip reading and provides a detailed discussion of the trade-offs between various approaches. It gives a reverse chronological topic wise listing of the developments in lip reading systems in recent years. With advancement in computer vision and pattern recognition tools, the efficacy of real time, effective conversion has increased. The major goal of this paper is to provide a comprehensive reference source for the researchers involved in lip reading, not just for the esoteric academia but all the people interested in this field regardless of particular application areas.
Towards Battery-Free HD Video Streaming

Video streaming has traditionally been considered an extremely power-hungry operation. Existing approaches optimize the camera and communication modules individually to minimize their power consumption. However, designing a video streaming device requires power-consuming hardware components and computationally intensive video codec algorithms that interface the camera and the communication modules. For example, monochrome HD video streaming at 60 fps requires an ADC operating at a sampling rate of 55.3 MHz and a video codec that can handle uncompressed data being generated at 442 Mbps. We present a novel architecture that enables HD video streaming from a low-power, wearable camera to a nearby mobile device. To achieve this, we present an “analog” video backscatter technique that feeds analog pixels from the photo-diodes directly to the backscatter hardware, thereby eliminating power-consuming hardware components, such as ADCs and codecs. To evaluate our design, we simulate an ASIC, which achieves 60 fps 720p and 1080p HD video streaming for 321 μW and 806 μW, respectively. This translates to 1000x to 10,000x lower power than it used for existing digital video streaming approaches. Our empirical results also show that we can harvest sufficient energy to enable battery-free 30 fps 1080p video streaming at up to 8 feet. Finally, we design and implement a proof-of-concept prototype with off-theshelf hardware components that successfully backscatters 720p HD video at 10 fps up to 16 feet.
Cleft Septorhinoplasty: Form and Function.

Nasal deformity associated with typical cleft lip can cause aesthetic and functional issues that are difficult to address. The degree of secondary nasal deformity is based on the extent of the original cleft deformity, growth over time, and any prior surgical correction to the nose or lip. Repair and reconstruction of these deformities require comprehensive understanding of embryologic growth, the cleft anatomy, as well as meticulous surgical technique and using a spectrum of structural grafting. This article reviews cleft lip nasal deformity, presurgical care, primary cleft rhinoplasty, and definitive cleft septorhinoplasty with a focus on aesthetics and function.
A Framework for Automatic Exam Generation based on Intended Learning Outcomes

Assessment plays important role in learning process in higher education institutions. However, poorly designed exams can fail to achieve the intended learning outcomes of a specific course, which can also have a bad impact on the programs and educational institutes. One of the possible solutions is to standardize the exams based on educational taxonomies. However, this is not an easy process for educators. With the recent technologies, the assessment approaches have been improved by automatically generating exams based on educational taxonomies. This paper presents a framework that allow educators to map questions to intended learning outcomes based on Bloom’s taxonomy. Furthermore, it elaborates on the principles and requirements for generating exams automatically. It also report on a prototype implementation of an authoring tool for generating exams to evaluate the achievements of intended learning outcomes.