nils-herrmann/clean_scirepeval huggingface dataset:
A secure end-to-end verifiable e-voting system using zero knowledge based blockchain

In this paper, we present a cryptographic technique for an authenticated, end-to-end verifiable and secret ballot election. Voters should receive assurance that their vote is cast as intended, recorded as cast and tallied as recorded. The election system as a whole should ensure that voter coercion is unlikely, even when voters are willing to be influenced. Currently, almost all verifiable e-voting systems require trusted authorities to perform the tallying process. An exception is the DRE-i and DRE-ip system. The DRE-ip system removes the requirement of tallying authorities by encrypting ballot in such a way that the election tally can be publicly verified without decrypting cast ballots. However, the DRE-ip system necessitates a secure bulletin board (BB) for storing the encrypted ballot as without it the integrity of the system may be lost and the result can be compromised without detection during the audit phase. In this paper, we have modified the DRE-ip system so that if any recorded ballot is tampered by an adversary before the tallying phase, it will be detected during the tallying phase. In addition, we have described a method using zero knowledge based public blockchain to store these ballots so that it remains tamper proof. To the best of our knowledge, it is the first end-toend verifiable Direct-recording electronic (DRE) based e-voting system using blockchain. In our case, we assume that the bulletin board is insecure and an adversary has read and write access to the bulletin board. We have also added a secure biometric with government provided identity card based authentication mechanism for voter authentication. The proposed system is able to encrypt ballot in such a way that the election tally can be publicly verified without decrypting cast ballots maintaining end-to-end verifiability and without requiring the secure bulletin board.
Maritime inventory routing with multiple products: A case study from the cement industry

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.08.023 ⇑ Corresponding author.  This paper considers a maritime inventory routing problem faced by a major cement producer. A heterogeneous fleet of bulk ships transport multiple non-mixable cement products from producing factories to regional silo stations along the coast of Norway. Inventory constraints are present both at the factories and the silos, and there are upper and lower limits for all inventories. The ship fleet capacity is limited, and in peak periods the demand for cement products at the silos exceeds the fleet capacity. In addition, constraints regarding the capacity of the ships’ cargo holds, the depth of the ports and the fact that different cement products cannot be mixed must be taken into consideration. A construction heuristic embedded in a genetic algorithmic framework is developed. The approach adopted is used to solve real instances of the problem within reasonable solution time and with good quality solutions.
Testing Deep Neural Networks

Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety critical domains. However, traditional software testing methodology, including test coverage criteria and test case generation algorithms, cannot be applied directly to DNNs. This paper bridges this gap. First, inspired by the traditional MC/DC coverage criterion, we propose a set of four test criteria that are tailored to the distinct features of DNNs. Our novel criteria are incomparable and complement each other. Second, for each criterion, we give an algorithm for generating test cases based on linear programming (LP). The algorithms produce a new test case (i.e., an input to the DNN) by perturbing a given one. They encode the test requirement and a fragment of the DNN by fixing the activation pattern obtained from the given input example, and then minimize the difference between the new and the current inputs. Finally, we validate our method on a set of networks trained on the MNIST dataset. The utility of our method is shown experimentally with four objectives: (1) bug finding; (2) DNN safety statistics; (3) testing efficiency and (4) DNN internal structure analysis.
An anatomical substrate for integration among functional networks in human cortex.

The human brain shows several characteristics of an efficient communication network architecture, including short communication paths and the existence of modules interlinked by a small set of highly connected regions. Studies of structural networks comprising macroscopic white matter projections have shown that these putative hubs are densely interconnected, giving rise to a spatially distributed and topologically central collective called the "rich club." In parallel, studies of intrinsic brain activity have consistently revealed distinct functional communities or resting-state networks (RSNs), indicative of specialized processing and segregation of neuronal information. However, the pattern of structural connectivity interconnecting these functional RSNs and how such inter-RSN structural connections might bring about functional integration between RSNs remain largely unknown. Combining high-resolution diffusion weighted imaging with resting-state fMRI, we present novel evidence suggesting that the rich club structure plays a central role in cross-linking macroscopic RSNs of the human brain. Rich club hub nodes were present in all functional networks, accounted for a large proportion of "connector nodes," and were found to coincide with regions in which multiple networks overlap. In addition, a large proportion of all inter-RSN connections were found to involve rich club nodes, and these connections participated in a disproportionate number of communication paths linking nodes in different RSNs. Our findings suggest that the brain's rich club serves as a macroscopic anatomical substrate to cross-link functional networks and thus plays an important role in the integration of information between segregated functional domains of the human cortex.
Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice

We report a technique for two-photon fluorescence imaging with cellular resolution in awake, behaving mice with minimal motion artifact. The apparatus combines an upright, table-mounted two-photon microscope with a spherical treadmill consisting of a large, air-supported Styrofoam ball. Mice, with implanted cranial windows, are head restrained under the objective while their limbs rest on the ball's upper surface. Following adaptation to head restraint, mice maneuver on the spherical treadmill as their heads remain motionless. Image sequences demonstrate that running-associated brain motion is limited to approximately 2-5 microm. In addition, motion is predominantly in the focal plane, with little out-of-plane motion, making the application of a custom-designed Hidden-Markov-Model-based motion correction algorithm useful for postprocessing. Behaviorally correlated calcium transients from large neuronal and astrocytic populations were routinely measured, with an estimated motion-induced false positive error rate of <5%.
SEXUAL MURDER AND SEXUAL SERIAL KILLERS : TOWARDS A MIND-BODY DEVELOPMENTAL THEORY

Sexual murders and sexual serial killers have always been of popular interest with the public. Professionals are still mystified as to why sexual killers commit the “ultimate crime” of both sexual assault and homicide. Questions emerge as to why some sexual offenders kill one time vs in a serial manner. It is understood that the vast majority of sexual offenders such as pedophiles and adult rapists do NOT kill their victims. The purpose of this chapter is to explore serial sexual murder in terms of both theoretical and clinical parameters in an attempt to understand why they commit the “ultimate crime.” We will also examine the similarities and differences between serial sexual murderers and typical rape offenders who do not kill their victims. Using real-life examples of wellknown serial killers, we will compare the “theoretical” with the “practical;” what happened, why it happened, and what we may be able to do about it. The authors of this chapter present two perspectives: (1) A developmental motivational view as to why serial killers commit these homicides, and (2) Implications for treatment of violent offenders. To adequately present these perspectives, we must look at four distinct areas: (1) Differentiating between the two types of “lust” murderers i.e. rapists and sexual serial killers, (2) Examining personality or lifestyle themes, (3) Exploration of the mind-body developmental process, and (4) treatment applications for violent offenders.
Toward a Computational Theory of Mind

Scientific knowledge of the brain and technology of intelligent systems has developed to a point where a computational theory of mind is feasible. This paper briefly describes the RCS (Real-time Control System) reference model architecture that has been used successfully over the past 30 years for designing intelligent systems for a wide variety of applications. It then suggests how RCS can be mapped onto the neuronal structure of the brain, and vice versa. Both RCS and the brain are goal-directed and sensory-interactive intelligent control systems. Both are hierarchical in structure and partitioned into behavior generating and sensory processing hierarchies. Both rely heavily on an internal model of the external world for perception and behavior. The world model is used in perception for focusing attention, segmentation, grouping, prediction, and classification. It is used in behavior for decision-making, planning, and control. Both RCS and the brain have value judgment processes that assign worth to perceived objects, events, situations, and scenarios; and estimate the cost, risk, and benefit of plans for future behavior. The formal structure of RCS provides a framework for a computational theory of mind that is both quantitative and experimentally testable.
Deciding where to attend: Large-scale network mechanisms underlying attention and intention revealed by graph-theoretic analysis

The neural mechanisms by which intentions are transformed into actions remain poorly understood. We investigated the network mechanisms underlying spontaneous voluntary decisions about where to focus visual-spatial attention (willed attention). Graph-theoretic analysis of two independent datasets revealed that regions activated during willed attention form a set of functionally-distinct networks corresponding to the frontoparietal network, the cingulo-opercular network, and the dorsal attention network. Contrasting willed attention with instructed attention (where attention is directed by external cues), we observed that the dorsal anterior cingulate cortex was allied with the dorsal attention network in instructed attention, but shifted connectivity during willed attention to interact with the cingulo-opercular network, which then mediated communications between the frontoparietal network and the dorsal attention network. Behaviorally, greater connectivity in network hubs, including the dorsolateral prefrontal cortex, the dorsal anterior cingulate cortex, and the inferior parietal lobule, was associated with faster reaction times. These results, shown to be consistent across the two independent datasets, uncover the dynamic organization of functionally-distinct networks engaged to support intentional acts.
A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual stereo SLAM

This work addresses the development and application of a novel approach, called sparser relative bundle adjustment (SRBA), which exploits the inherent flexibility of the relative bundle adjustment (RBA) framework to devise a continuum of strategies, ranging from RBA with linear graphs to classic bundle adjustment (BA) in global coordinates, where submapping with local maps emerges as a natural intermediate solution. This method leads to graphs that can be optimized in bounded time even at loop closures, regardless of the loop length. Furthermore, it is shown that the pattern in which relative coordinate variables are defined among keyframes has a significant impact on the graph optimization problem. By using the proposed scheme, optimization can be done more efficiently than in standard RBA, allowing the optimization of larger local maps for any given maximum computational cost. The main algorithms involved in the graph management, along with their complexity analyses, are presented to prove their bounded-time nature. One key advance of the present work is the demonstration that, under mild assumptions, the spanning trees for every single keyframe in the map can be incrementally built by a constant-time algorithm, even for arbitrary graph topologies. We validate our proposal within the scope of visual stereo simultaneous localization and mapping (SLAM) by developing a complete system that includes a front-end that seamlessly integrates several state-of-the-art computer vision techniques such as ORB features and bag-ofwords, along with a decision scheme for keyframe insertion and a SRBA-based back-end that operates as graph optimizer. Finally, a set of experiments in both indoor and outdoor conditions is presented to test the capabilities of this approach. Open-source implementations of the SRBA back-end and the stereo front-end have been released online.
Interference mitigation characteristics of SIW-based CP-generating array antenna

This paper describes the comparison of the mutual coupling mechanisms between two different feeding structures such as coplanar waveguides and SIW-based feeding in array antenna for the application of satellite communication. It is usually well-know that both feeding structures of co-planar waveguides and SIWs are good candidates for the design of high frequency antenna with the help of easy integration, implementation with other active and passive devices, and low-loss characteristics. Hence, the interference or coupling mechanism in array antenna is investigated and comparison will be carried out according to the feeding types. In addition, in the proposed array working at X-band ranging from 8 to 8.5GHz for the application of satellite communication, 2X2 array antennas using sequential feeding are designed using CPW and SIW structures. In particular, it is shown that the interference level due to the SIW structure has been significantly improved rather than that of CPW structure by using a commercially available full-EM software.
Mapping the internal geometry of tactile space.

A large body of research has shown spatial distortions in the perception of tactile distances on the skin. For example, perceived tactile distance is increased on sensitive compared to less sensitive skin regions, and larger for stimuli oriented along the medio-lateral axis than the proximo-distal axis of the limbs. In this study we aimed to investigate the spatial coherence of these distortions by reconstructing the internal geometry of tactile space using multidimensional scaling (MDS). Participants made verbal estimates of the perceived distance between 2 touches applied sequentially to locations on their left hand. In Experiment 1 we constructed perceptual maps of the dorsum of the left hand, which showed a good fit to the actual configuration of stimulus locations. Critically, these maps also showed clear evidence of spatial distortion, being stretched along the medio-lateral hand axis. Experiment 2 replicated this result and showed that no such distortion is apparent on the palmar surface of the hand. These results show that distortions in perceived tactile distance can be characterized by geometrically simple and coherent deformations of tactile space. We suggest that the internal geometry of tactile space is shaped by the geometry of receptive fields in somatosensory cortex.
Node-Based Genetic Algorithm for Communication Spanning Tree Problem

Genetic Algorithm (GA) and other Evolutionary Algorithms (EAs) have been successfully applied to solve constrained minimum spanning tree (MST) problems of the communication network design and also have been used extensively in a wide variety of communication network design problems. Choosing an appropriate representation of candidate solutions to the problem is the essential issue for applying GAs to solve real world network design problems, since the encoding and the interaction of the encoding with the crossover and mutation operators have strongly influence on the success of GAs. In this paper, we investigate a new encoding crossover and mutation operators on the performance of GAs to design of minimum spanning tree problem. Based on the performance analysis of these encoding methods in GAs, we improve predecessor-based encoding, in which initialization depends on an underlying random spanning-tree algorithm. The proposed crossover and mutation operators offer locality, heritability, and computational efficiency. We compare with the approach to others that encode candidate spanning trees via the Pr?fer number-based encoding, edge set-based encoding, and demonstrate better results on larger instances for the communication spanning tree design problems. key words: minimum spanning tree (MST), communication network design, genetic algorithm (GA), node-based encoding
Pattern-based Information Extraction and Diagnostics

This paper aims to provide an effective interface for progressive refinement of pattern-based information extraction systems. Pattern-based information extraction (IE) systems have an advantage over machine learning based systems that patterns are easy to customize to cope with errors and are interpretable by humans. Building a pattern-based system is usually an iterative process of trying different parameters and thresholds to learn patterns and entities with high precision and recall. Since patterns are interpretable to humans, it is possible to identify sources of errors, such as patterns responsible for extracting incorrect entities and vice-versa, and correct them. However, it involves time consuming manual inspection of the extracted output. We present a light-weight tool, SPIED, to aid IE system developers in learning entities using patterns with bootstrapping, and visualizing the learned entities and patterns with explanations. SPIED is the first publicly available tool to visualize diagnostic information of multiple pattern learning systems to the best of our knowledge.
Current and future trends in AI

During the past 70+ years of research and development in the domain of Artificial Intelligence (AI) we observe three principal, historical waves: embryonic, embedded and embodied AI. As the first two waves have demonstrated huge potential to seed new technologies and provide tangible business results, we describe likely developments of embodied AI in the next 25-35 years. We postulate that the famous Turing Test was a noble goal for AI scientists, making key, historical inroads - while we believe that Biological Systems Intelligence and the Insect/Swarm Intelligence analogy/mimicry, though largely disregarded, represents the key to further developments. We describe briefly the key lines of past and ongoing research, and outline likely future developments in this remarkable field.
A Survey of Device-to-Device Communications: Research Issues and Challenges

Device-to-Device (D2D) communication has emerged as a promising technology for optimizing spectral efficiency in future cellular networks. D2D takes advantage of the proximity of communicating devices for efficient utilization of available resources, improving data rates, reducing latency, and increasing system capacity. The research community is actively investigating the D2D paradigm to realize its full potential and enable its smooth integration into the future cellular system architecture. Existing surveys on this paradigm largely focus on interference and resource management. We review recently proposed solutions in over explored and under explored areas in D2D. These solutions include protocols, algorithms, and architectures in D2D. Furthermore, we provide new insights on open issues in these areas. Finally, we discuss potential future research directions.
A detailed survey on auto irrigation system

Agriculture is the backbone of world's economy. The important infrastructure for the development of agriculture sector is irrigation. The rapid increase in urbanization and industrialization has caused a large lacuna in agriculture activities, infrastructure, and labor. Irrigation and agriculture have become a very important area of concern and is on the radar of researchers. To overcome the world's major problem, there are many works being done to automate the agricultural sector and irrigation in particular. The developments in the field are reported independently and there is not a single place where a comprehensive literature survey may be found. Thus, this paper discusses the detailed work carried out on auto irrigation system across the globe and India in particular. This paper will be very helpful for future researchers to carry out their research and have comprehensive analysis.
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots

We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy parameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters. We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: 1) learning the inverse kinematics in a highly-redundant robotic arm, 2) learning omnidirectional locomotion with motor primitives in a quadruped robot, 3) an arm learning to control a fishing rod with a flexible wire. We show that 1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; 2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly, as well as more efficient than different standard active motor babbling methods; 3) this architecture allows the robot to actively discover which parts of its task space it can learn to reach and which part it cannot.