nils-herrmann/clean_scirepeval huggingface dataset:
A Review in Detection and Monitoring Gait Disorders Using In-Shoe Plantar Measurement Systems

Gait is an important part of our life, as it affects many daily activities. Special footwear is fundamental to obtain an ergonomic gait and to extract data for analysis. The plantar foot pressure can be employed to detect many kinds of disorders, suggest improvements in treatments, rehabilitation tasks, patient monitoring, development of orthopedic devices, and other applications. In recent years, attention to this topic has grown and is reflected in many works issued in both commercial and academic groups, and has focused on the development of devices for foot plantar pressure measurement with applications in medicine, sports, and research. First works on this subject appeared around 1963 and have continuously evolved with emerging technologies. This paper reviews the reported developments in the field of footwear-embedded sensors for gait measurement, monitoring, diagnosis, and analysis in rehabilitation. Future work is proposed to improve the field of measurement of the footprint with electronic devices.
Teaching a Machine to Read Maps With Deep Reinforcement Learning

The ability to use a 2D map to navigate a complex 3D environment is quite remarkable, and even difficult for many humans. Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community. In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before. Our system combines several state-of-theart methods such as A3C and incorporates novel elements such as a recurrent localization cell. Our agent learns to localize itself based on 3D first person images and an approximate orientation angle. The agent generalizes well to bigger mazes, showing that it learned useful localization and naviga-
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes

In this work we study the quantitative relation between the recursive teaching dimension (RTD) and the VC dimension (VCD) of concept classes of finite sizes. The RTD of a concept class C ⊆ {0, 1}, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in C according to the recursive teaching model. For any finite concept class C ⊆ {0, 1} with VCD(C) = d, Simon & Zilles (2015) posed an open problem RTD(C) = O(d), i.e., is RTD linearly upper bounded by VCD? Previously, the best known result is an exponential upper bound RTD(C) = O(d · 2), due to Chen et al. (2016). In this paper, we show a quadratic upper bound: RTD(C) = O(d), much closer to an answer to the open problem. We also discuss the challenges in fully solving the problem.
Parameterized Pattern Matching: Algorithms and Applications

The problem of finding sections of code that either are identical or are related by the systematic renaming of variables or constants can be modeled in terms of parameterized strings ( p-strings) and parameterized matches ( p-matches). P-strings are strings over two alphabets, one of which represents parameters. Two p-strings are a parameterized match ( p-match) if one p-string is obtained by renaming the parameters of the other by a one-to-one function. In this paper, we investigate parameterized pattern matching via parameterized suffix trees (p-suffix trees). We give two algorithms for constructing p-suffix trees: one (eager) that runs in linear time for fixed alphabets, and another that uses auxiliary data structures and runs in O(n log(n)) time for variable alphabets, where n is input length. We show that using a p-suffix tree for a pattern p-string P, it is possible to search for all p-matches of P within a text p-string T in space linear in |P| and time linear in |T | for fixed alphabets, or O( |T | log(min( |P|, _)) time and O( |P|) space for variable alphabets, where _ is the sum of the alphabet sizes. The simpler p-suffix tree construction algorithm eager has been implemented, and experiments show it to be practical. Since it runs faster than predicted by the above worst-case bound, we reanalyze the algorithm and show that eager runs in time O(min(t |S |+m(t, S) | t>0) log _)), where for an input p-string S, m(t, S) is the number of maximal p-matches of length at least t that occur within S, and _ is the sum of the alphabet sizes. Experiments with the author's program dup (B. Baker, in `` 1992) for finding all maximal p-matches within a p-string have found m(t, S) to be less than |S | in practice unless t is small.
UMAN WAYFINDING IN UNFAMILIAR BUILDINGS : A SIMULATION WITH A COGNIZING AGENT

Existing cognitively based computational models for wayfinding focus primarily on the exploration of mental representations rather than the information needs for wayfinding. It is important to consider information needs because people trying to find their ways in unfamiliar environments do not have a previously acquired mental representation but depend on external information. The fundamental tenet of this work is that all information must be presented to the wayfinder at each decision point as “knowledge in the world.” Simulating people’s wayfinding behavior in a cognitively plausible way requires the integration of structures for information perception and cognition in the underlying model. In this work we use a cognizing agent to simulate people’s wayfinding processes in unfamiliar buildings. The agent-based model consists of two tiers: simulated states of the environment and simulated beliefs of the agent. The agent is modeled with state, an observation schema, a specific wayfinding strategy, and commonsense knowledge. The environment is modeled as a graph, where nodes represent decision points and edges represent lines of movement. The perceptual wayfinding model integrates the agent and its environment within a “Sense-Plan-Act” framework. It focuses on knowledge in the world to explain actions of the agent. The concepts of affordance and information are used to describe the kinds of knowledge the agent derives from the world by means of visual perception. Affordances are possibilities for action with reference to the agent. Information is necessary for the agent to decide which affordances to utilize. During the navigation process the agent accumulates beliefs about the environment by observing task-relevant affordances and information at decision points. The utilization of a “go-to” affordance leads the agent from one node to another where it is again provided with percepts. A successful navigation corresponds to the agent’s traversal from a start to a goal node. The proposed formal specifications of the agent-based model can be used to simulate people’s wayfinding behavior in spatial information and design systems in a cognitively plausible way. Such simulation helps to determine where and why people face wayfinding difficulties and what needs to be done to avoid them. The case of wayfinding in an airport, in which the signage in the airport is tested, is employed to demonstrate the perceptual wayfinding model.
A 1.2–6.6GHz LNA using transformer feedback for wideband input matching and noise cancellation in 0.13µm CMOS

A novel transformer feedback featuring wideband input matching and noise cancellation is proposed and demonstrated in a wideband differential LNA for software-defined-radio (SDR) applications. Implemented in 0.13μm CMOS with an area of 0.32mm2, the LNA prototype measures a wideband input matching S11 of less than -10dB from 1.2GHz to 6.6GHz and minimum NF of 1.8dB while consuming 11mA at 1.2V supply.
No-reference image and video quality assessment: a classification and review of recent approaches

The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art
The lexical constituency model: some implications of research on Chinese for general theories of reading.

The authors examine the implications of research on Chinese for theories of reading and propose the lexical constituency model as a general framework for word reading across writing systems. Word identities are defined by 3 interlinked constituents (orthographic, phonological, and semantic). The implemented model simulates the time course of graphic, phonological, and semantic priming effects, including immediate graphic facilitation followed by graphic inhibition with simultaneous phonological facilitation, a pattern unique to the Chinese writing system. Pseudocharacter primes produced only facilitation, supporting the model's assumption that lexical thresholds determine phonological and semantic, but not graphic, effects. More generally, both universal reading processes and writing system constraints exist. Although phonology is universal, its activation process depends on how the writing system structures graphic units.
Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.
Social recommendations for personalized fitness assistance

Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile and keep track of their goals. However, due to the relatively “high maintenance” of such applications, where a significant amount of user feedback is expected, users who are very busy, or not as self-motivated, stop using them after a while. It has been shown that accountability improves commitment to an exercise routine. In this work, we present the PRO-Fit framework, a personalized fitness assistant aiming at engaging users in fitness activities, incorporating a social element. The PRO-Fit architecture collects information from activity tracking devices and automatically classifies their activity type. Moreover, the framework incorporates a social recommender system. Using collaborative filtering on user profile and activity data, PRO-Fit generates personalized fitness schedules based on their availability and wellbeing goals. We also incorporate the social network community of the application’s users and identify different tie strengths based on the user’s connections and location. The output of the recommendation process is twofold, as both new activities, as well as fitness buddies, are being recommended to each user.
Human skin detection and face recognition using fuzzy logic and eigenface

Human skin detection and face recognition is a very active area for research in the field of image processing. It is used to prevent many crimes and helps to provide public security. Due to the high complexity in techniques such as real time processing and image contents analysis/understanding, a well-developed product is not available until now in this field. In this work, human presence is detected from an image by extracting the skin region (if present). Since human skin can be found in a varied colour, fuzzy approach is used to extract the skin region. After that individual faces are detected. Eigenface is used to recognize different faces. The experimental results show the effectiveness of the proposed technique.
Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's “tactile sensor.” This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.
Role-Based Access Controls

While Mandatory Access Controls (MAC) are appropriate for multilevel secure military applications, Discretionary Access Controls (DAC) are often perceived as meeting the security processing needs of industry and civilian government. This paper argues that reliance on DAC as the principal method of access control is unfounded and inappropriate for many commercial and civilian government organizations. The paper describes a type of non-discretionary access control role-based access control (RBAC) ­ that is more central to the secure processing needs of non-military systems than DAC.
A randomized trial of two different doses of a SHR-5 Rhodiola rosea extract versus placebo and control of capacity for mental work.

A randomized, double-blind, placebo-controlled, parallel-group clinical study with an extra non-treatment group was performed to measure the effect of a single dose of standardized SHR-5 Rhodiola rosea extract on capacity for mental work against a background of fatigue and stress. An additional objective was to investigate a possible difference between two doses, one dose being chosen as the standard mean dose in accordance with well-established medicinal use as a psychostimulant/adaptogen, the other dose being 50% higher. Some physiological parameters, e.g. pulse rate, systolic and diastolic blood pressure, were also measured. The study was carried out on a highly uniform population comprising 161 cadets aged from 19 to 21 years. All groups were found to have very similar initial data, with no significant difference with regard to any parameter. The study showed a pronounced antifatigue effect reflected in an antifatigue index defined as a ratio called AFI. The verum groups had AFI mean values of 1.0385 and 1.0195, 2 and 3 capsules respectively, whilst the figure for the placebo group was 0.9046. This was statistically highly significant (p < 0.001) for both doses (verum groups), whilst no significant difference between the two dosage groups was observed. There was a possible trend in favour of the lower dose in the psychometric tests. No such trend was found in the physiological tests.
Robust Subspace Clustering via Smoothed Rank Approximation

Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for this problem which can recover the rank exactly under some restricted and theoretically interesting conditions. However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum. To seek a solution of higher accuracy than the nuclear norm, in this letter, we propose a rank approximation based on Logarithm-Determinant. We consider using this rank approximation for subspace clustering application. Our framework can model different kinds of errors and noise. Effective optimization strategy is developed with theoretical guarantee to converge to a stationary point. The proposed method gives promising results on face clustering and motion segmentation tasks compared to the state-of-the-art subspace clustering algorithms.
A micropower dry-electrode ECG preamplifier

This paper describes the development of a very low-power preamplifier intended for use in pasteless-electrode recording of the human electrocardiogram. The expected input signal range is 100 /spl mu/V-10 mV from a lead-II electrode configuration. The amplifier provides a gain of 43 dB in a 3-dB bandwidth of 0.05 Hz-2 kHz with a defined high input impedance of 75 M/spl Omega/. It uses a driven common electrode to enhance rejection of common-mode interfering signals, including low-frequency motion artifact, achieving a common-mode rejection ratio (CMRR) of better than 80 dB over its entire bandwidth. The gain and phase characteristics meet the recommendations of the American Heart Association, ensuring low distortion of the output ECG signal and making it suitable for clinical monitoring. The amplifier has a power consumption of 30 /spl mu/W operating from a 3.3-V battery and is intended for use in small, lightweight, portable electrocardiographic equipment and heart-rate monitoring instrumentation.
Self-training on refined clause patterns for relation extraction

Within the context of Information Extraction (IE), relation extraction is oriented towards identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE), and 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that help reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state-of-the-art techniques.
Item-based top-N recommendation algorithms

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.
Robust Formulations of the Cauchy Method Suitable for Microwave Duplexers Modeling

It is well known that a Vandermonde matrix generates an ill-conditioned system matrix when applied with finite numerical precision. This deficiency affects the Cauchy method by restricting its application to only lower order systems. This paper presents innovative, accurate, and robust formulations of the Cauchy method to rectify this limitation and make the Cauchy method suitable for the extraction of a high-order microwave duplexer polynomial model. The techniques employed are: the change of polynomial basis into the Krylov subspace and the precondition technique, both acting on the system matrix of the classic Cauchy method formulation. A novel formulation using the QR algorithm on the two characteristic functions of the duplexer and a suitable combination of the QR method and the precondition technique are then presented. Each of these procedures has been successfully verified by numerical application examples