nils-herrmann/clean_scirepeval huggingface dataset:
Indoor positioning in Bluetooth networks using fingerprinting and lateration approach

Global Positioning System (GPS) is a well known navigation system for outdoor applications; however this technology does not work in indoor environments. In order to overcome this limitation, Bluetooth technology can be employed. Furthermore, Bluetooth technology provides an accurate and low cost solution for short range wireless communication. Most of the digital devices provide the Bluetooth functionality which also makes it a good candidate for indoor positioning. In this paper, we use Bluetooth devices for indoor positioning and use signal based parameters such as received power level for position estimation. The accuracy of indoor positioning system is greatly dependant on the parameters selected for estimation and the measurements obtained from the environment. However, the measurements are corrupted by various environmental conditions such as temperature, reflection, presence of obstacles, human body and other communication signals. Therefore, we need to filter the measurements. This paper presents an experimental relationship between the received power level and distance using the standard radio propagation model. The idea behind this study is to provide an accurate distance estimate for Trilateration approach. Based on the experiments performed, the average error is minimized from 5.87 meters to 2.67 meters using gradient filter. The use of gradient filter improves the accuracy by 45 %.
Enhanced Security of Data using Image Steganography and AES Encryption Technique

Today Security of data is of foremost importance in today&apos;s world. Security has become one of the most important factor in communication and information technology. For this purpose steganography is used. Steganography is the art of hiding secret or sensitive information into digital media like images so as to have secure communication. In this paper we present and discuss LSB (Least Significant Bit) based image steganography and AES
A Survey of Feature Base Methods for Human Face Detection

The human face is among the most significant objects in an image or video, it contains many important information and specifications, also is required to be the cause of almost all achievable look variants caused by changes in scale, location, orientation, pose, facial expression, lighting conditions and partial occlusions. It plays a key role in face recognition systems and many other face analysis applications. We focus on the feature based approach because it gave great results on detect the human face. Face feature detection techniques can be mainly divided into two kinds of approaches are Feature base and image base approach. Feature base approach tries to extract features and match it against the knowledge of the facial features. This paper gives the idea about challenging problems in the field of human face analysis and as such, as it has achieved a great attention over the last few years because of its many applications in various domains. Furthermore, several existing face detection approaches are analyzed and discussed and attempt to give the issues regarding key technologies of feature base methods, we had gone direct comparisons of the method's performance are made where possible and the advantages/ disadvantages of different approaches are discussed.
Stevioside and related compounds - molecules of pharmaceutical promise: a critical overview.

Stevioside, an ent-kaurene type of diterpenoid glycoside, is a natural sweetener extracted from leaves of Stevia rebaudiana (Bertoni) Bertoni. Stevioside and a few related compounds are regarded as the most common active principles of the plant. Such phytochemicals have not only been established as non-caloric sweeteners, but reported to exhibit some other pharmacological activities also. In this article, natural distribution of stevioside and related compounds, their structural features, plausible biosynthetic pathways along with an insight into the structure-sweetness relationship are presented. Besides, the pharmacokinetics, wide-range of pharmacological potentials, safety evaluation and clinical trials of these ent-kaurene glycosides are revisited.
CertCoin : A NameCoin Based Decentralized Authentication System 6 . 857 Class Project

Authentication is vital to all forms of remote communication. A lack of authentication opens the door to man-in-the-middle attacks, which, if performed at a key moment, may subvert the entire interaction. Current approaches to authentication on the internet include certificate authorities and webs of trust. Both of those approaches have significant drawbacks: the former relies upon trusted third parties, introducing a central point of failure, and the latter has a high barrier to entry. We propose Certcoin, an alternative, public and decentralized authentication scheme. The core idea of Certcoin is maintaining a public ledger of domains and their associated public keys. We describe the Certcoin scheme, as well as several optimizations to make Certcoin more accessible to devices with limited storage capacity, such as smartphones. Our optimizations use tools such as cryptographic accumulators and distributed hash tables.
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions

Visual Question Answering (VQA) has attracted attention from both computer vision and natural language processing communities. Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image using pre-trained attribute detectors and image captioning models, respectively. Next, a reasoning module utilizes these explanations in place of the image to infer an answer to the question. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some explanations for the predicted answer; (2) these intermediate results can help us identify the inabilities of both the image understanding part and the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and dissect all results according to several measurements of the explanation quality. Our system achieves comparable performance with the state-of-theart, yet with added benefits of explanability and the inherent ability to further improve with higher quality explanations.
Dynamic Few-Shot Visual Learning Without Forgetting

The human visual system has the remarkably ability to be able to effortlessly learn novel concepts from only a few examples. Mimicking the same behavior on machine learning vision systems is an interesting and very challenging research problem with many practical advantages on real world vision applications. In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories). To achieve that goal we propose (a) to extend an object recognition system with an attention based few-shot classification weight generator, and (b) to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors. The latter, apart from unifying the recognition of both novel and base categories, it also leads to feature representations that generalize better on "unseen" categories. We extensively evaluate our approach on Mini-ImageNet where we manage to improve the prior state-of-the-art on few-shot recognition (i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings respectively) while at the same time we do not sacrifice any accuracy on the base categories, which is a characteristic that most prior approaches lack. Finally, we apply our approach on the recently introduced few-shot benchmark of Bharath and Girshick  where we also achieve state-of-the-art results.
Neuroanatomical characterisation of Alzheimer ’ s disease using deep learning

Alzheimer’s disease (AD) is an incurable neurological condition which causes progressive mental deterioration, especially in the elderly. The focus of our work is to improve our understanding about the progression of AD. By finding brain regions which degenerate together in AD we can understand how the disease progresses during the lifespan of an Alzheimer’s patient. Our aim is to work towards not only achieving diagnostic performance but also generate useful clinical information.  The main objective of this study is to find important sub regions of the brain which undergo neuronal degeneration together during AD using deep learning algorithms and other machine learning techniques.  We extract 3D brain region patches from 100 subject MRI images using a predefined anatomical atlas. We have devised an ensemble of pair predictors which use 3D convolutional neural networks to extract salient features for AD from a pair of regions in the brain. We then train them in a supervised manner and use a boosting algorithm to find the weightage of each pair predictor towards the final classification. We use this weightage as the strength of correlation and saliency between the two input sub regions of the pair predictor.  We were able to retrieve sub regional association measures for 100 sub region pairs using the proposed method. Our approach was able to automatically learn sub regional association structure in AD directly from images. Our approach also provides an insight into computational methods for demarcating effects of AD from effects of ageing (and other neurological diseases) on our neuroanatomy. Our meta classifier gave a final accuracy of 81.79% for AD classification relative to healthy subjects using a single imaging modality dataset.
1 Kernels for graphs

This chapter discusses the construction of kernel functions between labeled graphs. We provide a unified account of a family of kernels called label sequence kernels that are defined via label sequences generated by graph traversal. For cyclic graphs, dynamic programming techniques cannot simply be applied, because the kernel is based on an infinite dimensional feature space. We show that the kernel computation boils down to obtaining the stationary state of a discrete-time linear system, which is efficiently performed by solving simultaneous linear equations. Promising empirical results are presented in classification of chemical compounds.
A Patch Antenna With a Varactor-Loaded Slot for Reconfigurable Dual-Band Operation

A new design approach for a microstrip patch antenna to achieve reconfigurable dual-band operation with tunable frequency ratio is introduced. The approach uses a lumped capacitor in the middle of a slotted patch antenna, which results in two resonant frequencies. The two resonant frequencies and their ratio are functions of the capacitance value. If a varactor with an appropriate biasing network is used, electronic tuning is realized by changing the applied DC voltage. To understand the dual-resonance behavior of the proposed antenna, an equivalent circuit model based on the transmission line theory of the antenna is established, considering the slot effect and the lumped capacitor. The results are verified with full wave simulation. Furthermore, measurements for fabricated antenna prototypes operating in 2-4.5 GHz are carried out for validation, and the performance shows a tunable frequency ratio from 1.45 to 1.93 with a capacitance range from 0.31 to 0.74 pF. It is worthwhile to point out that the radiation patterns are similar at both bands because both resonances are due to the fundamental TM01 mode.
Network Effects and Personal Influences : The Diffusion of an Online Social Network

We study the diffusion process in an online social network given the individual connections between members. We model the adoption decision of individuals as a binary choice affected by three factors: (1) the local network structure formed by already adopted neighbors, (2) the average characteristics of adopted neighbors (influencers), and (3) the characteristics of the potential adopters. Focusing on the first factor, we find two marked effects. First, an individual who is connected to many adopters has a higher adoption probability (degree effect). Second, the density of connections in a group of already adopted consumers has a strong positive effect on the adoption of individuals connected to this group (clustering effect). We also record significant effects for influencer and adopter characteristics. Specifically, for adopters, we find that their position in the entire network and some demographic variables are good predictors of adoption. Similarly, in the case of already adopted individuals, average demographics and global network position can predict their influential power on their neighbors. An interesting counter-intuitive finding is that the average influential power of individuals decreases with the total number of their contacts. These results have practical implications for viral marketing in a context where, increasingly, a variety of technology platforms are considering to leverage their consumers’ revealed connection patterns. In particular, our model performs well in predicting the next set of adopters.
Understanding the exploding gradient problem

Training Recurrent Neural Networks is more troublesome than feedforward ones because of the vanishing and exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to understand the fundamental issues underlying the exploding gradient problem by exploring it from an analytical, a geometric and a dynamical system perspective. Our analysis is used to justify the simple yet effective solution of norm clipping the exploded gradient. In the experimental section, the comparison between this heuristic solution and standard SGD provides empirical evidence towards our hypothesis as well as it shows that such a heuristic is required to reach state of the art results on a character prediction task and a polyphonic music prediction one.
CHAPTER 7 Grounded Theory as an Emergent Method

D uring its 40-year history, grounded theory has served as a major method for conducting emergent qualitative research. What is an emergent method? I start with a working definition of an emergent method as inductive, indeterminate, and open-ended. An emergent method begins with the empirical world and builds an inductive understanding of it as events unfold and knowledge accrues. Social scientists who use emergent methods can study research problems that arise in the empirical world and can pursue unanticipated directions of inquiry in this world. Emergent methods are particularly well suited for studying uncharted, contingent, or dynamic phenomena. These methods also allow for new properties of the studied phenomenon to appear that, in turn, shape new conditions and consequences to be studied. By adopting emergent methods, researchers can account for processes discovered in the empirical world and direct their methodological strategies accordingly. How does grounded theory fit the definition of an emergent method? In which ways does the grounded theory method advance the development of emergent methods? Grounded theory is predicated on an emergent logic. This method starts with a systematic, inductive approach to collecting and analyzing data to develop theoretical analyses. The method also includes checking emergent categories that emerge from successive levels of analysis through hypothetical and deductive reasoning. Grounded theory offers systematic analytic strategies that combine explicitness and flexibility. Fundamental tenets of the grounded theory method include: (1) minimizing preconceived ideas about the research problem and the data, (2) using simultaneous data collection and analysis to inform each other, (3) remaining open to varied explanations and/ or understandings of the data, and (4) focusing data analysis to construct middle-range theories. Rather than viewing only the product of inquiry—the completed grounded the-
Hardware for machine learning: Challenges and opportunities

Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). In many applications, machine learning often involves transforming the input data into a higher dimensional space, which, along with programmable weights, increases data movement and consequently energy consumption. In this paper, we will discuss how these challenges can be addressed at various levels of hardware design ranging from architecture, hardware-friendly algorithms, mixed-signal circuits, and advanced technologies (including memories and sensors).
MorphoJ: an integrated software package for geometric morphometrics.

Increasingly, data on shape are analysed in combination with molecular genetic or ecological information, so that tools for geometric morphometric analysis are required. Morphometric studies most often use the arrangements of morphological landmarks as the data source and extract shape information from them by Procrustes superimposition. The MorphoJ software combines this approach with a wide range of methods for shape analysis in different biological contexts. The program offers an integrated and user-friendly environment for standard multivariate analyses such as principal components, discriminant analysis and multivariate regression as well as specialized applications including phylogenetics, quantitative genetics and analyses of modularity in shape data. MorphoJ is written in Java and versions for the Windows, Macintosh and Unix/Linux platforms are freely available from http://www.flywings.org.uk/MorphoJ_page.htm.
Recurrent Attentional Networks for Saliency Detection

Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.
A Standalone RFID Indoor Positioning System Using Passive Tags

Indoor positioning systems (IPSs) locate objects in closed structures such as office buildings, hospitals, stores, factories, and warehouses, where Global Positioning System devices generally do not work. Most available systems apply wireless concepts, optical tracking, and/or ultrasound. This paper presents a standalone IPS using radio frequency identification (RFID) technology. The concept is based on an object carrying an RFID reader module, which reads low-cost passive tags installed next to the object path. A positioning system using a Kalman filter is proposed. The inputs of the proposed algorithm are the measurements of the backscattered signal power propagated from nearby RFID tags and a tag-path position database. The proposed algorithm first estimates the location of the reader, neglecting tag-reader angle-path loss. Based on the location estimate, an iterative procedure is implemented, targeting the estimation of the tag-reader angle-path loss, where the latter is iteratively compensated from the received signal strength information measurement. Experimental results are presented, illustrating the high performance of the proposed positioning system.
Snowbot : An empirical study of building chatbot using seq 2 seq model with different machine learning framework

Chatbot is a growing topic, we built a open domain generative chatbot using seq2seq model with different machine learning framework (Tensorflow, MXNet). Our result show although seq2seq is a successful method in neural machine translation, use it solely on single turn chatbot yield pretty unsatisfactory result. Also existing free dialog corpus lacks both quality and quantity. Our conclusion it’s hard to build a useful open domain generative bot using state of art technology.
Re-Visiting the Knowledge Pyramid

The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom. This paper posits that the knowledge pyramid is too basic and fails to represent reality and presents a revised knowledge pyramid. One key difference is that the revised knowledge pyramid includes knowledge management as an extraction of reality with a focus on organizational learning. The model also posits that newer initiatives such as business and/or customer intelligence are the result of confusion in understanding the traditional knowledge pyramid that is resolved in the revised knowledge pyramid.
Low Levels of Empathic Concern Predict Utilitarian Moral Judgment

Is it permissible to harm one to save many? Classic moral dilemmas are often defined by the conflict between a putatively rational response to maximize aggregate welfare (i.e., the utilitarian judgment) and an emotional aversion to harm (i.e., the non-utilitarian judgment). Here, we address two questions. First, what specific aspect of emotional responding is relevant for these judgments? Second, is this aspect of emotional responding selectively reduced in utilitarians or enhanced in non-utilitarians? The results reveal a key relationship between moral judgment and empathic concern in particular (i.e., feelings of warmth and compassion in response to someone in distress). Utilitarian participants showed significantly reduced empathic concern on an independent empathy measure. These findings therefore reveal diminished empathic concern in utilitarian moral judges.
Explainable Entity-based Recommendations with Knowledge Graphs

Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items. When review text is unavailable, generating explanations is still a hard problem. In this paper, we illustrate how explanations can be generated in such a scenario by leveraging external knowledge in the form of knowledge graphs. Our method jointly ranks items and knowledge graph entities using a Personalized PageRank procedure to produce recommendations together with their explanations.