nils-herrmann/clean_scirepeval huggingface dataset:
A Machine Learning Approach to Demographic Prediction using Geohashes

With the rapid proliferation of smartphones, human beings act as social sensors by means of carrying GPS-enabled devices that share location data. This has resulted in an abundance of sensor data gathered over long periods of time. Gaining meaningful insights from such massive amounts of spatio-temporal data accumulated by several disparate sources is often a challenge for organizations. Identifying demographics of mobile phone users by telecommunication providers is one such example. Demographic information plays a very significant role in targeting online advertisements to focused user groups by gaining insights about userfis mobility patterns. However, in practice, demographic information such as age and gender are mostly unavailable to app developers for open access due to privacy concerns. In this paper, we try to address the gap of how to enrich location data with demographics, which could be valuable for app developers. In our study, we use a machine learning approach to predict the gender and age of mobile phone users from a set of 3,252,950 anonymised GPS trajectories with 60,865 unique devices using a predictive model which is based upon the concept of Geohashes. We study to what extent usersfi demographics could be inferred from their frequently visited locations by encoding by formulating a multi-level classification algorithm to find the most frequently visited Geohashes and associating them with nearest points of interests which would enable predicting age-group and gender of the users who prefer to visit a specific location in a sequential manner. Experiments are conducted on a real dataset of mobile phone users collected and shared by a telecommunication provider. Th The experimental results show that the proposed algorithm can achieve mean prediction accuracy scores of 71.62% and 96.75% for predicting gender and age groups of the users respectively.
Comparison of Shunt and Series/Shunt nMOS Single-Pole Double-Throw Switches for X-Band Phased Array T/R Modules

This paper compares the performance of shunt and series/shunt single-pole double-throw nMOS switches designed in a 0.13 mum SiGe BiCMOS process for X-band phased array transmit/receive modules. From 8.5 to 10.5 GHz, the worst case return loss, insertion loss, and isolation are 14.5, 1.89, and 20.5 dB, respectively, for the reflective shunt switch, and 22.2, 2.33, and 22.5 dB, respectively, for the absorptive series/shunt switch. Both switches exhibit an IIP3 of about 28 dBm and dissipate no dc power. The performance of these switches are comparable to other CMOS switches found in triple well technologies, on non-standard substrates, using special device structures, or using extra dc biases
TechWare: Financial Data and Analytic Resources [Best of the Web]

In this issue, “Best of the Web” focuses on data resources and analytic tools for quantitative analysis of financial markets. An abundance of financial data is reshaping trading, investment research, and risk management. The broad availability of this information creates opportunities to introduce analysis techniques that are new to the financial industry. The financial industry is currently dominated by a handful of workhorse models, such as the capital asset pricing model and the Black-Scholes options pricing model.
Frostbite following cryolipolysis treatment in a beauty salon: a case study.

This case study describes frostbite, a previously unreported complication following cryolipolysis, which resulted in substantial necrosis of the flank. Medical attention was not sought until one week after treatment. On examination, two distinct areas of significant frostbite in the left flank with surrounding erythema were revealed. Surgical intervention was avoided, as is recommended in cases of frostbite, and conservative treatment resulted in recovery of the affected area. Here, the authors highlight the adverse effects related to cryolipolysis, analysing the pathogenesis, clinical manifestations and management of this injury. The necessity of regulation within the cosmetic sector and the challenges associated with its implementation are also described. The authors believe emphasis must be placed on increasing patient awareness on the potential hazards of seeking cosmetic treatment from unregulated providers.
Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm

Feeding of the world in the 21st century is the biggest challenge, especially for smart farm business. The smart farm has used agriculture automation system instead of traditional agriculture. Traditional agricultural methods employed by the local people are highly sustainable, although the all inclusive cost is not cheap. Our research goal is to provide long term sustainable solution for automation of agriculture. Agriculture automation has several methods to getting data from vegetable crop like sensor for environmental measurement. Therefore, we developed a portable measurement technology including soil moisture sensor, air humidity sensor and air temperature sensor. Moreover, irrigation system using wireless sensor network has installed these sensors, with the purpose for collecting the environment data and controlling the irrigation system via smart phone. The purpose of the experiment is to find better ways of controlling an irrigation system with automatic system and manual control by smart phone. In order to control an irrigation system, we have developed the communication methodology of the wireless sensor network for collected environment data and sending control command to turn on/off irrigation system. It is successful for controlling the irrigation system and controlling the water near the vegetable roots. In this paper, we have attempted to demonstrate the automation of the irrigation system that is useful for farm business which make it comfortable than using traditional agriculture by using smart phone for monitoring and controlling the system. Accordingly, in the long-term has reduced cost as well. The experimental result shows that the accuracy of sending and receiving command control for irrigation system is 96 percent and accuracy of environment collection is 98 percent.
Online social networks and offline protest

Large-scale protests occur frequently and sometimes overthrow entire political systems. Meanwhile, online social networks have become an increasingly common component of people’s lives. We present a large-scale longitudinal study that connects online social media behaviors to offline protest. Using almost 14 million geolocated tweets and data on protests from 16 countries during the Arab Spring, we show that increased coordination of messages on Twitter using specific hashtags is associated with increased protests the following day. The results also show that traditional actors like the media and elites are not driving the results. These results indicate social media activity correlates with subsequent large-scale decentralized coordination of protests, with important implications for the future balance of power between citizens and their states.
A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes

Due to the significant growth of video data over the Internet, video steganography has become a popular choice. The effectiveness of any steganographic algorithm depends on the embedding efficiency, embedding payload, and robustness against attackers. The lack of the preprocessing stage, less security, and low quality of stego videos are the major issues of many existing steganographic methods. The preprocessing stage includes the procedure of manipulating both secret data and cover videos prior to the embedding stage. In this paper, we address these problems by proposing a novel video steganographic method based on Kanade-Lucas-Tomasi (KLT) tracking using Hamming codes (15, 11). The proposed method consists of four main stages: a) the secret message is preprocessed using Hamming codes (15, 11), producing an encoded message, b) face detection and tracking are performed on the cover videos, determining the region of interest (ROI), defined as facial regions, c) the encoded secret message is embedded using an adaptive LSB substitution method in the ROIs of video frames. In each facial pixel 1 LSB, 2 LSBs, 3 LSBs, and 4 LSBs are utilized to embed 3, 6, 9, and 12 bits of the secret message, respectively, and d) the process of extracting the secret message from the RGB color components of the facial regions of stego video is executed. Experimental results demonstrate that the proposed method achieves higher embedding capacity as well as better visual quality of stego videos. Furthermore, the two preprocessing steps increase the security and robustness of the proposed algorithm as compared to state-of-the-art methods.
Examining the Contribution of Information Technology Toward Productivity and Profitability in U . S . Retail Banking

There has been much debate on whether or not the investment in Information Technology (IT) provides improvements in productivity and business efficiency. Several studies both at the industry-level and at the firm-level have contributed differing understandings of this phenomenon. Of late, however, firm-level studies, primarily in the manufacturing sector, have shown that there are significant positive contributions from IT investments toward productivity. This study examines the effect of IT investment on both productivity and profitability in the retail banking sector. Using data collected through a major study of retail banking institutions in the United States, this paper concludes that additional investment in IT capital may have no real benefits and may be more of a strategic necessity to stay even with the competition. However, the results indicate that there are substantially high returns to increase in investment in IT labor, and that retail banks need to shift their emphasis in IT investment from capital to labor. Prasad and Harker IT Impact in Retail Banking
When Asperger's Disorder Came Out.

In 2013, the American Psychiatric Association removed Asperger's Disorder from the DSM, offering instead the new DSM-5 diagnosis: Autism Spectrum Disorder. This change has been hailed the most controversial exclusion from the DSM, yet unlike the 1973 removal of homosexuality from DSM-III, Asperger's disorder has not been demedicalised. Rather, the disorder has simply been reclassified as part of the DSM-5 Autism Spectrum and therefore retains its fundamental characteristic as a mental disorder owing to its inclusion within the sphere of the DSM.



This paper is based on a review of the current academic literature in conjunction with careful reading of the DSM-5.



Removing the Asperger's label, valued by patients for its distinctiveness from autism brings with it the potential to inflict iatrogenic harm.



This paper demonstrates how the DSM-5 reclassification has the potential to threaten the identity of those affected, and discusses the problem of autism as a stigmatizing diagnostic label.



A case is made for the use of tandem social/colloquial - medical/technical terminology to refer to the conditions classified under DSM-5 Autism Spectrum Disorder, in order to square the circle of social concerns regarding identity and stigma with the need for diagnostic clarity to continue to advance medical practice.
Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) systems in bacteria and archaea use RNA-guided nuclease activity to provide adaptive immunity against invading foreign nucleic acids. Here, we report the use of type II bacterial CRISPR-Cas system in Saccharomyces cerevisiae for genome engineering. The CRISPR-Cas components, Cas9 gene and a designer genome targeting CRISPR guide RNA (gRNA), show robust and specific RNA-guided endonuclease activity at targeted endogenous genomic loci in yeast. Using constitutive Cas9 expression and a transient gRNA cassette, we show that targeted double-strand breaks can increase homologous recombination rates of single- and double-stranded oligonucleotide donors by 5-fold and 130-fold, respectively. In addition, co-transformation of a gRNA plasmid and a donor DNA in cells constitutively expressing Cas9 resulted in near 100% donor DNA recombination frequency. Our approach provides foundations for a simple and powerful genome engineering tool for site-specific mutagenesis and allelic replacement in yeast.
Computing label-constraint reachability in graph databases

Our world today is generating huge amounts of graph data such as social networks, biological networks, and the semantic web. Many of these real-world graphs are edge-labeled graphs, i.e., each edge has a label that denotes the relationship between the two vertices connected by the edge. A fundamental research problem on these labeled graphs is how to handle the label-constraint reachability query: Can vertex u reach vertex v through a path whose edge labels are constrained by a set of labels? In this work, we introduce a novel tree-based index framework which utilizes the directed maximal weighted spanning tree algorithm and sampling techniques to maximally compress the generalized transitive closure for the labeled graphs. An extensive experimental evaluation on both real and synthetic datasets demonstrates the efficiency of our approach in answering label-constraint reachability queries.
Going digital: understanding paper and photo documentation practices in early childhood education

Documentation of development is a critical aspect of the work of early childhood education teachers. Through field observations and interviews, we detail the process and tools used in documenting development for children age three months to five-years-old at one school. Teachers use paper forms and printed photos to record and analyze observations of development. The evolving final product is a paper portfolio of development. This practice relies heavily on the teacher's ability to objectively observe children in situ, create a record of the activity, and make salient the link between evidence and developmental milestones. We describe current paper and photo documentation practices in light of an online record keeping system that will be introduced at this school within the next year. The present analysis contributes to a growing literature on the role of paper and digital media in documentation.
Distributed intelligence for multi-camera visual surveillance

Latest advances in hardware technology and state of the art of computer vision and arti,cial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the di0erent components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed arti,cial intelligence composed of separate and autonomous modules is justi,ed by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and e4ciently built by modular software. ? 2003 Pattern Recognition Society.
Benefits from Using Continuous Rating Scales in Online Survey Research

The usage of Likert-type scales has become widespread practice in current IS research. Those scales require individuals to choose between a limited number of choices, and have been criticized in the literature for causing loss of information, allowing the researcher to affect responses by determining the range, and being ordinal in nature. The use of online surveys allows for the easy implementation of continuous rating scales, which have a long history in psychophysical measurement but were rarely used in IS surveys. This type of measurement requires survey participants to express their opinion in a visual form, i.e. to place a mark at an appropriate position on a continuous line. That not only solves the problems of information loss, but also allows for applying advanced robust statistical analyses. In this 1 Augasse 2-6, A-1090 Vienna, Austria Tel.: +43/1/31336/4480 Fax: +43/1/31336/746 E-Mail: Horst.Treiblmaier@wu.ac.at
Field-Programmable Deep Neural Network (DNN) Learning and Inference accelerator: a concept

An accelerator is a specialized integrated circuit designed to perform specific computations faster than if those computations were performed by general purpose processor, CPU or GPU. State-of-the-art Deep Neural Networks (DNNs) are becoming progressive larger and different applications require different number of layers, types of layer, number of nodes per layer and different interconnect between consecutive layers. A DNN learning and inference accelerator thus need to be reconfigurable against those many requirements for a DNN. It needs to reconfigure to maximum use its on die resources, and if necessary, need to be able to connect with other similar dies or packages for larger and higher performing DNNs. A Field-Programmable DNN learning & inference accelerator (FProg-DNN) using hybrid systolic/non-systolic techniques, distributed information/control and deep pipelined structure is proposed and its microarchitecture and operation presented here. 400mm die sizes are planned for 100 thousand workers (FP64) that can extend to multipledie packages. Reconfigurability allows for different number of workers to be assigned to different layers as a function of the relative difference in computational load among layers. The computational delay per layer is made roughly the same along pipelined accelerator structure. VGG-16 and recently proposed Inception Modules are used for showing the flexibility of the FProg-DNN’s reconfigurability. Special structures were also added for a combination of convolution layer, map coincidence and feedback for state of the art learning with small set of examples, which is the focus of a companion paper by the author (Franca-Neto, 2018). The flexibility of the accelerator described can be appreciated by the fact that it is able to reconfigure from (1) allocating all a DNN computations to a single worker in one extreme of sub-optimal performance to (2) optimally allocating workers per layer according to computational load in each DNN layer to be realized. Due the pipelined nature of the DNN realized in the FProgDNN, the speed-up provided by FProg-DNN varies from 2x to 50x to GPUs or TPUs at equivalent number of workers. This speed-up is consequence of hiding the delay in transporting activation outputs from one layer to the next in a DNN behind the computations in the receiving layer. This FProg-DNN concept has been simulated and validated at behavioral/functional level.
Two Methods for Semi-automatic Image Segmentation based on Fuzzy Connectedness and Watersheds

At the present time, one of the best methods for semiautomatic image segmentation seems to be the approach based on the fuzzy connectedness principle. First, we identify some deficiencies of this approach and propose a way to improve it, through the introduction of competitive learning. Second, we propose a different approach, based on watersheds. We show that the competitive fuzzy connectedness-based method outperforms the noncompetitive variant and generally (but not always) outperforms the watershed-based approach. The competitive variant of the fuzzy connectedness-based method can be a good alternative to the watersheds.
A Category-Based Model for ABAC

In Attribute-Based Access Control (ABAC) systems, access to resources is controlled by evaluating rules against the attributes of the user and the object involved in the access request, as well as the values of the relevant attributes from the environment. This is a powerful concept: ABAC is able to enforce DAC and RBAC policies, as well as more general, dynamic access control policies, where the decision to grant or deny an access request is based on the system's state. However, in its current definition, ABAC does not lend itself well to some operations, such as review queries, and it is in general more costly to specify and maintain than simpler systems such as RBAC. To address these issues, in this paper we propose a formal model of ABAC based on the notion of a category that underlies the general category-based metamodel of access control (CBAC). Our proposed approach adds structure to ABAC, so that policies are easier to design and understand, review queries become easy to evaluate, and simple systems such as RBAC can be implemented as instances of ABAC without additional costs.
A Hybrid Wired/Wireless Networking Infrastructure for Greenhouse Management

In this paper, the problems related to the management of a farm made up of several greenhouses are discussed. The management of this kind of farms requires data acquisition in each greenhouse and their transfer to a control unit which is usually located in a control room, separated from the production area. At present, the data transfer between the greenhouses and the control system is mainly provided by a suitable wired communication system, such as a fieldbus. In such contexts, even though the replacement of the wired system with a fully wireless one can appear very attractive, a fully wireless system can introduce some disadvantages. A solution based on a hybrid wired/wireless network, where Controller Area Network and ZigBee protocols are used, is presented along with all the related problems that this integration involves. In particular, in order to integrate at the Data Link Layer the wireless section with the wired one, a suitable multiprotocol bridge has been implemented. Moreover, at the Application Layer, porting of Smart Distributed System services on ZigBee, called ZSDS, allows one to access the network resources independently from the network segment they are connected to.
Real-Time Classification of Dance Gesturesfrom Skeleton Animation

We present a real-time gesture classification system for skeletal wireframe motion. Its key components include an angular representation of the skeleton designed for recognition robustness under noisy input, a cascaded correlation-based classifier for multivariate time-series data, and a distance metric based on dynamic time-warping to evaluate the difference in motion between an acquired gesture and an oracle for the matching gesture. While the first and last tools are generic in nature and could be applied to any gesture-matching scenario, the classifier is conceived based on the assumption that the input motion adheres to a known, canonical time-base: a musical beat. On a benchmark comprising 28 gesture classes, hundreds of gesture instances recorded using the XBOX Kinect platform and performed by dozens of subjects for each gesture class, our classifier has an average accuracy of 96:9%, for approximately 4-second skeletal motion recordings. This accuracy is remarkable given the input noise from the real-time depth sensor.