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opencc-by-4.0Mar 2023View details →

When fear meets anger: Attitudes toward positively versus negatively evaluated pandemic policy proposals when negative emotions are competing in society

Since the outbreak of the COVID‐19 pandemic, citizens of many countries have been faced with health‐related fear, as well as anti‐establishment and anti‐governmental anger. This emotional landscape colored the ongoing efforts by the authorities to convince citizens to accept various public policy proposals. In two studies (total N = 528, one preregistered) conducted in Poland in two different situations, we focused on the role of the simultaneously evoked pandemic fear and anti‐government anger in shaping attitudes toward the pandemic regulations. For negatively evaluated proposals, both of these emotions worked in opposite directions: fear was associated with increasing support, while anger was associated with increasing rejection. However, for positively evaluated policy proposals, fear and anger worked in consonance, and both were associated with increasing acceptance of the proposed regulations. Thus, while fear seems to motivate the acceptance of even negatively evaluated proposals that are seen as protective ones, anger works to amplify or polarize the proposals’ basic evaluations. Our findings could help plan the implementation of public policies in societies in times of turbulent emotional landscapes.

Figure 7 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 7. Reconstruction of the postpharyngeal genital organs of Dreuxiola philippi sp. nov., seen from the right. Refer to the Appendix for a list of abbreviations.

Figure 4 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 4. Hard parts of the copulatory organ of Meidiama uruguayensis sp. nov. (A; inset, terminal opening in the holotype), Meidiama lutheri (B), and Meidiama schockaerti (C).

Figure 15 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 15. The needles and the muscular supports of the copulatory organ in the species of Serrula. A, Serrula byronensis sp. nov.; B, Serrula maxillaria sp. nov.; C, Serrula concharum sp. nov.; D, Serrula acuta sp. nov. Refer to the Appendix for a list of abbreviations.

Figure 3 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 3. Reconstruction of the copulatory organ in Meidiama lutheri and Meidiama uruguayensis sp. nov., seen from the left, based on sections of M. uruguayensis sp. nov. and the whole mounts of both species. Refer to the Appendix for a list of abbreviations.

Figure 14 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 14. Posteriormost body part in live animals. A, Serrula byronensis sp. nov.; B, Serrula maxillaria sp. nov.; C, Serrula concharum sp. nov. Refer to the Appendix for a list of abbreviations.

Figure 2 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 2. Sagittal reconstruction of the female system, seen from the left, of the species of Meidiama, mainly based on sections of Meidiama uruguayensis sp. nov. The lower figure is the continuation of the upper part. Refer to the Appendix for a list of abbreviations.

Figure 5. A in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 5. A living animal of Dreuxiola philippi sp. nov. Refer to the Appendix for a list of abbreviations.

Figure 11 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 11. Sagittal reconstruction of the postpharyngeal genital organs of Yorknia aprostatica sp. nov., seen from the left. Refer to the Appendix for a list of abbreviations.

Figure 12 in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 12. Copulatory organ of Yorknia aprostatica sp. nov., as seen in the whole mounts. A, in the holotype; B, in the paratype. Refer to the Appendix for a list of abbreviations.

Figure 10. Drawings from a in A new family of lithophoran Proseriata (Platyhelminthes), with the description of seven new species from the Indo-Pacific and South America, and the proposal of three new genera

Figure 10. Drawings from a living animal of Yorknia aprostatica sp. nov. A, the whole animal; B, the postpharyngeal genital organs. Refer to the Appendix for a list of abbreviations.

opencc-by-4.0Nov 2023View details →

Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.

The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.It consists of the following layers:<ul><li>conv1: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li>relu1: Rectified Linear Unit (3@28x28).</li><li>max1: Subsampling buy max pooling (3@14x14).</li><li>conv2: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li>relu2: Rectified Linear Unit (6@14x14).</li><li>max2: Subsampling buy max pooling (6@7x7).</li><li>fc1: Fully connected (294, 147)</li><li>fc2: Fully connected (147, 10)</li></ul>The fault hypotheses for this work include the occurrence of:<ul><li>S0/S1: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li>BF: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul>In the memory cells containing all the parameters of the CNN:  <ul><li>w: weights (float32)</li><li>b: biases (float32)</li></ul>All the images (10000) from the MNIST dataset have been used as workload.The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.The parity check matrices (H = P I) that define these ECCs are:<ul><li>SEC(32, 26) (Hamming) under a classic policy (see methodology below):</li></ul>        11010010001000011101101000 100000        10101001000100011011010100 010000        01100100100010010110110010 001000        00011100010001001110001101 000100        00000011110000100001111011 000010        00000000001111100000000111 000001<ul><li>SEC(23, 18) (Hamming) under a conservative policy (see methodology below):</li></ul>        111100001111000000 10000        110011101000111000 01000        101011010100100110 00100        010110110010010101 00010        001101110001001011 00001<ul><li>SEC(13, 9) (Hamming) under an aggressive policy (see methodology below):</li></ul>        110111000 1000        101100110 0100        011010101 0010        111001011 0001<ul><li>DEC(32, 21) (low redundancy and reduced overhead DEC) under a classic policy (see methodology below):</li></ul>        111000011001010010000 10000000000        110110000011101000000 01000000000        101011000110000010001 00100000000        100101101000110001000 00010000000        011010101100100000100 00001000000        010101010100001001010 00000100000        001100110010010100100 00000010000        000011110001000110010 00000001000        000000001111001101001 00000000100        000000000000111100111 00000000010        000000000000000011111 00000000001<ul><li>DEC(28, 18) (low redundancy and reduced overhead DEC) under a conservative policy (see methodology below):</li></ul>        111111000000000000 1000000000        110100111100000000 0100000000        110000100011110000 0010000000        001110010011001100 0001000000        101100001010101010 0000100000        010001001101010110 0000010000        001011000101101001 0000001000        101000011000110101 0000000100        010001110000011011 0000000010        000010100110000111 0000000001<ul><li>DEC(17, 9) (low redundancy and reduced overhead DEC) under an aggressive policy (see methodology below):</li></ul>        111110000 10000000        111001100 01000000        110101010 00100000        101010110 00010000        101101001 00001000        100110101 00000100        100011011 00000010        110000111 00000001This dataset contains the raw data obtained from:<ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li>no_ecc folder: Results obtained for the original (not protected) version of the CNN.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li>locating_sensitive_bits folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li>sec/classic, sec/conservative, and sec/aggressive folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li>dec/classic, dec/conservative, and dec/aggressive folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is golden_run.csv file.To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:<ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul>The analysis of the obtained results may help in determining which bits can be used to store an ECC:<ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul>Accordingly, three different policies have been identified for deploying an ECC using these bits:<ul><li>Classic policy: The ECC protects as much bits as possible.</li><li>Conservative policy: The ECC protects all those bits that may affect the prediction of the network.</li><li>Aggressive policy: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.Each experiment consisted in:<ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li>IMGID: Integer number identifying the considered image (1-9999).</li><li>TENSORID: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li>ELEMID: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li>MASK: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li>FAULT: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li>SOFTMAX: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li>PRED: Integer number representing the category predicted for the processed image.</li><li>LABEL: integer number representing the actual category for the processed image.</li></ul>

opencc-by-4.0Apr 2024View details →

Direct Measurement of Learning Outcomes in Engineering Programs: A Proposal for Nine Standardized Scales

This database presents the results of nine different scales aimed at directly evaluating learning outcomes as generic attributes in engineering programs, as defined by the Washington Accord and the International Alliance of Engineering. Data was collected at a higher education institution focused on engineering and technology as part of quality assurance processes. Each scale features a distinct number of indicators. The data correspond to the following scales: AC (Lifelong Learning), AF (Project Management and Finance), AP (Problem Analysis), DI (Design/Development of Solutions), EE (Ethics), HC (Communication), HI (Tool Usage), IN (Investigation), and TE (Individual and Collaborative Teamwork).

Figure 16 in Description of two new species of Neotachidius Shen & Tai, 1963 (Copepoda, Harpacticoida, Tachidiidae) from Korean brackish waters and proposal of a new genus for Tachidius (Tachidius) vicinospinalis Shen & Tai, 1964

Figure 16. SEM micrographs of P2 endopod ♂. Neotachidius coreanus sp. nov. (A, C); N. parvus sp. nov. (B).

Figure 14 in Description of two new species of Neotachidius Shen & Tai, 1963 (Copepoda, Harpacticoida, Tachidiidae) from Korean brackish waters and proposal of a new genus for Tachidius (Tachidius) vicinospinalis Shen & Tai, 1964

Figure 14. Neotachidius parvus sp. nov. (♂) A, antennulary segments 5–6, anterior; B, urosome, ventral (integumental pores obscured by spinules arrowed); C, urosome, lateral. N. coreanus sp. nov. (♂) D, antennulary segments 5–6, anterior (armature of segment 5 omitted; setation element with ornamentation different from N. parvus arrowed in inset); E, antennulary segment 7, anterior (modified elements arrowed; posterior setae and acrothek omitted).

Figure 13 in Description of two new species of Neotachidius Shen & Tai, 1963 (Copepoda, Harpacticoida, Tachidiidae) from Korean brackish waters and proposal of a new genus for Tachidius (Tachidius) vicinospinalis Shen & Tai, 1964

Figure 13. Neotachidius parvus sp. nov. A, P2 ♀, anterior; B, P2 endopod ♂, anterior (distal outer element arrowed); C, P5 ♀, anterior; D, anal somite and right caudal ramus ♀, dorsal.

Figure 15 in Description of two new species of Neotachidius Shen & Tai, 1963 (Copepoda, Harpacticoida, Tachidiidae) from Korean brackish waters and proposal of a new genus for Tachidius (Tachidius) vicinospinalis Shen & Tai, 1964

Figure 15. Neotachidius parvus sp. nov. (♂) SEM micrographs of antennule: A, segments around geniculation, anterior; B, vestigial segment 5, anterior (arrowed); C, apical segment, dorsal; D, segment 6, anterior (a = longitudinally ribbed modified element; b = multicuspidate process).

Figure 12 in Description of two new species of Neotachidius Shen & Tai, 1963 (Copepoda, Harpacticoida, Tachidiidae) from Korean brackish waters and proposal of a new genus for Tachidius (Tachidius) vicinospinalis Shen & Tai, 1964

Figure 12. Neotachidius parvus sp. nov. A, segment contours of ♀ antennule; B, antenna ♀; C, P1 ♀, anterior (outer distal seta of enp-3 arrowed); D, P3 ♀, anterior (proximal part of protopod and most ornamentation omitted); E, P3 ♂, anterior (proximal part of protopod omitted).

Figure 10 in Description of two new species of Neotachidius Shen & Tai, 1963 (Copepoda, Harpacticoida, Tachidiidae) from Korean brackish waters and proposal of a new genus for Tachidius (Tachidius) vicinospinalis Shen & Tai, 1964

Figure 10. Neotachidius coreanus sp. nov. A, urosome ♂, ventral; B, antennule ♂ and free margin of cephalosome, dorsal (armature of segments 1–3 and seven complete; for complete armature of other segments see C, Fig. 14D); C, antennulary segments 1–6 ♂, anterior (armature of segment three omitted). D, anal somite and left caudal ramus ♀, dorsal (accessory dorsal spinular row on caudal ramus arrowed).

Figure 11 in Description of two new species of Neotachidius Shen & Tai, 1963 (Copepoda, Harpacticoida, Tachidiidae) from Korean brackish waters and proposal of a new genus for Tachidius (Tachidius) vicinospinalis Shen & Tai, 1964

Figure 11. Neotachidius parvus sp. nov. (♀) A, habitus, dorsal; B, urosome (excluding P5-bearing somite), ventral; C, P5-bearing and genital double-somite, lateral (P5 omitted).